Mycoplasma subtypes appear to vary. This study aimed to assess the correlations between common STIs and HPV infection, and to investigate the clinical significance of Mycoplasma subtypes.Human papillomavirus (HPV) infection, especially persistent high-risk HPV, is associated with cervical cancer. Female reproductive tract microecological disorders and lower genital tract infections have been increasingly correlated with HPV infection and cervical lesions. Due to their common risk factors and transmission routes, coinfection with other sexually transmitted infections (STIs) has become a concern. Additionally, the clinical significance of We recruited 1,175 patients undergoing cervical cancer screening at the Peking University First Hospital gynecological clinic from March 2021 to February 2022 for vaginitis and cervicitis tests. They all received HPV genotyping and detection of STIs, and 749 of them underwent colposcopy and cervical biopsy.adj: 1.810, 95%CI: 1.211–2.705, P=0.004; ORadj: 11.032, 95%CI: 1.465–83.056, P=0.020, respectively).Aerobic vaginitis/desquamative inflammatory vaginitis and STIs (mainly single STIs) were found significantly more often in the HPV-positive group than in the HPV-negative group. Among patients with a single STI, rates of infection with herpes simplex virus type 2 or UP6 in the HPV-positive group were significantly higher than in the HPV-negative group (ORMycoplasma typing, a correlation was found between different Mycoplasma subtypes and HPV infection. These findings suggest that greater attention should be paid to detecting vaginal microecological disorders in those who are HPV-positive. Further, lower genital tract infections, including both vaginal infections and cervical STIs, are significantly more common among women who are HPV-positive and who thus require more thorough testing. Detailed typing and targeted treatment of Mycoplasma should become more routine in clinical practice.Through detailed  Human papillomavirus (HPV) is among the most common sexually transmitted infections (STIs), and persistent high-risk HPV (HR-HPV) infection is associated with cervical cancer . More thChlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), herpes simplex virus type 2 (HSV-2), Mycoplasma hominis (MH), Mycoplasma genitalium (MG), Ureaplasma urealyticum (UU), and Ureaplasma parvum (UP). The World Health Organization estimates that CT infection, NG infection, syphilis, and TV together accounted for 357.4 million new infections globally in 2012 , aerobic vaginitis (AV), desquamative inflammatory vaginitis (DIV), trichomoniasis (TV), vulvovaginal candidiasis (VVC), and cervicitis, are associated with STIs. STIs are a significant medical problem affecting women’s health, with an estimated global daily incidence of 1 million . Common  in 2012 . The incfection” , it is nividuals .Mycoplasma, to investigate the correlation between common STIs and HPV infection. The aim of the study was to guide more efficient and comprehensive clinical examination and diagnosis.In this study we recruited subjects undergoing cervical cancer screening and tested them for genital tract infection, including carrying out detailed typing of This human study was reviewed and approved by the Ethics Committee of Peking University First Hospital (2021KY062). We collected data on 1,175 subjects who underwent cervical cancer screening at gynecological clinics of The First Hospital of Peking University from March 2021 to February 2022 and who met the following requirements: age 19–50 years; pre-menopausal; sexual history; not in the menses phase of their menstrual cycle; and abstention from intercourse or vaginal medication or irrigation for 3 days before sample collection. Cervical samples were collected from all enrolled subjects for molecular detection of HPV and STI pathogens. Those referred for colposcopy underwent comprehensive colposcopy and pathological biopsy of any abnormal cervical tissue. Exclusion criteria were as follows: pregnancy within the previous 8 weeks; vaginal bleeding; history of genital tract tumors; recent treatment for HPV infection or other STI; a history of hysterectomy, cervical surgery, pelvic radiotherapy, or cervical ablation or resection in the last 12 months; and use of antibiotics or probiotics within the past month Figure 1Samples were collected by professional gynecologists after standardized training. With the patient in the lithotomy position, vaginal secretions were collected with a swab at a standard anatomical site  and rolled onto a glass slide for immediate Gram staining to detect candidal hyphae and spores and clue cells. Exposing the cervix with a sterile speculum without lubricant, a sterile cotton swab was rotated five times in the cervical canal at a depth of 1–2 cm for 15–20 seconds before removal. Exfoliated cervical cells and secretions were obtained from the cervical epithelium with two cell brushes and stored at 4°C. They were then transferred to a buffer for DNA testing immediately or within 10 days, for separate genotyping of 21 HPV subtypes and STI pathogen detection.BV was diagnosed by using the Gram stain-based Nugent score   and, usiThe 21 HPV GenoArray Diagnostic Kit  was used, with the Rapid Capture System, using a HPV genotyping macroarray for HPV identification. The kit detects 14 HR-HPV types , one suspected HR-HPV type (HPV53), and six low-risk HPV (LR-HPV) types . We used the kit in accordance with the manufacturer’s instructions and analyzed the results of cell lysis, DNA extraction, polymerase chain reaction (PCR) amplification, and hybridization.A nucleic acid detection kit  was used to detect STI pathogens including UU, UP , CT, HSV-2, MH, MG, and NG. A real-time PCR fluorescence probe was used to detect the pathogenic STI microorganisms. This method was used for single and mixed infections.® cytologic test (TCT)  in accordance with the manufacturer’s instructions and double checked by cytotechnologists. Reported cytological results were classified in accordance with the 2001 Bethesda system  colposcopy standards , and coladj) and 95% CI were calculated. All tests were two-sided, and a p-value < 0.05 was considered statistically significant.IBM SPSS Statistics version 28.0  software was used for statistical analyses. Frequency data are described as a percentage of cases, and co-occurrence of HPV and other bacteria was compared using chi-squared and Fisher’s exact probability tests, as appropriate. The Bonferroni correction test level was 0.05/3 = 0.0168 for multiple comparisons between different age groups. Univariate logistic regression was used to analyze the relative risk  of HPV infection with different pathogens. After adjusting for age and AV/DIV infection status, the adjusted OR  age of 35.37 ± 6.773 years, and an approximately normal distribution. The overall HPV infection rate was 66.0% , with a monotypic infection rate of 54.2% (420/775) and a polytypic infection rate of 45.8% (355/775). The HR-HPV infection rate in the HPV-positive group was 95.7% (742/775), of which HPV16/18 infection accounted for 29.2% (217/742). Calculating the number of infections by HPV subtype revealed that the three most common HR-HPV subtypes were HPV16, HPV58, and HPV52, whereas the three most common low-grade HPV subtypes were HPV11, HPV CP8304, and HPV6 Figure 2The overall STI pathogen rate was 61.0% , of which single and multiple infections accounted for 68.9% (494/717) and 31.1% (223/717), respectively. The most prevalent infections were UP3 infection, at 20.2% , followed by UP6 infection, at 18.3% , UU infection, at 14.3% , and CT infection, at 11.3%  Figure 3p = 0.026, p = 0.011, There were no significant differences in BV, TV, or VVC infection rate between the groups with and without HPV infection; however, the AV/DIV infection rate was significantly higher in the HPV-positive group than in the HPV-negative group (p < 0.001) in the HPV-positive group. Considering that there were > 50 potential STI pathogen combinations in the multiple STI group, and that that were < 15 cases of any combination, this group was combined for analyses. Although there was no age group effect for HPV infection rate (inconsistent with many previous studies), logistic regression was performed after adjusting for age and AV/DIV infection status. The UP6  is well known, we also assessed whether STI pathogen status differed between groups with diverse cervical biopsy results after adjusting for HPV infection status among the 749 individuals referred for colposcopy. There was not a significant between-cervical biopsy group difference in either total STI pathogen rate or individual STI pathogen rates after adjusting for HPV infection status Table 6.p = 0.026), although no significant between-group differences in BV, TV, or VVC infection rates were observed. Previous investigators have shown a correlation between common vaginitis and HPV infection, suggesting that this is a risk factor . Furthermore, the UP6 infection rate was significantly higher in the HR-HPV-negative group than in the HR-HPV-positive group , suggesting that UP6 infection may be a risk factor for HPV infection. Our results are not uniformly consistent with those of previous studies, probably because of cohort differences or sample size limitations. Although the correlation between different UP serovars and HPV infection remains to be clearly established, based on our extant findings we nevertheless propose that further, detailed classification of Mycoplasma, especially different UP serovars, is needed to advance clinical practice.Detailed ining 10 . Few stuining 10  proposedining 10 . In theiining 10  found thining 10 . A retroining 10 . Moreoveining 10 . Analogoining 10  observedadj 11.032, 95% CI 1.465–83.056; p = 0.020). HSV-2 infection is among the most common STIs worldwide and is the leading cause of recurrent genital herpes. Many previous studies have used blood samples to detect HSV-2, partly because a specific kit for testing cervical samples was unavailable at the time. In this study, we used cervical samples to detect HSV-. Regarding the correlation between HSV-2 and HPV infection and cervical lesions, Smith et al. , suggesting that HSV-2 serostatus may serve as an independent predictor of cervical cancer. Our results appear to be consistent with these studies, though they are limited by the small sample size and the potential correlation between HSV-2 and HPV infection requires further study.Furthermore, the HPV infection rate group was significantly higher in the HSV-2-positive than in the HSV-2-negative group . The patients/participants provided their written informed consent to participate in this study.BX conceived the study design. BX, DZ, and HB recruited volunteers and collected samples. DA and BX performed the data analysis. DA wrote the initial manuscript. BX and DA revised the manuscript. All authors contributed to the article and approved the submitted version.


According to the results obtained in this observational study with real-world data, most participants repaired their cervical lesions and cleared HPV through Papilocare® treatment, with a good treatment tolerability, adherence, and satisfaction rates, regardless of being infected with high-risk HPV, age over 40 years or both of them. Data reported in this study strengthen the role of the vaginal gel Papilocare® as a complementary action to the simple watchful waiting approach in HPV-related low-grade cervical lesions management.Human papillomavirus (HPV) is responsible for virtually all cervical cancers in women. Although in the case of low-grade lesions the conventional approach is watchful waiting, some infections may lead to different-grade squamous intraepithelial lesions that can result in high-grade lesions and cancer. In this context, PapilocareCoriolus-versicolor-based vaginal gel (Papilocare®) on HPV-dependent low-grade cervical lesion repair in a real-life scenario. HPV-positive women ≥ 25 years with ASCUS/LSIL cervical cytology results and concordant colposcopy images were included, receiving the vaginal gel one cannula/day for 21 days (first month) + one cannula/alternate days (five months). A 6-month second treatment cycle was prescribed when needed. Repair of the cervical low-grade lesions through cytology and colposcopy, HPV clearance, and level of satisfaction, and tolerability were evaluated. In total, 192 and 201 patients accounted for the total and safety analyses, respectively, and 77.1% repaired cervical lesions at 6 or 12 months (76.0% for high-risk HPV). Additionally, 71.6% achieved HPV clearance throughout the study’s duration (70.6% for high-risk HPV). Satisfaction level was rated 7.9 and 7.5 out of 10 at 6 and 12 months, respectively. Only three mild–moderate product-related adverse events were reported, and all of them were resolved by the end of the study. In our study, we observed higher regression rates of low-grade cervical lesions in women treated with Papilocare® vaginal gel than spontaneous regression rates reported in the literature.Human papillomavirus (HPV) is responsible for virtually all cervical cancers in women. HPV infection and persistency may lead to different-grade squamous intraepithelial lesions that can result in high-grade lesions and cancer. The objective was to prospectively evaluate the results of using a  Human papillomavirus (HPV) is the most common sexually transmitted infection, with most sexually active men and women acquiring it during their lifetime . More thDue to the severe burden linked to this virus, therapies that deal with persistent HPV infection are essential. Despite the efficacy of vaccines in reducing HPV infections, these prophylactic and preventive methods are not effective in targeting already-established infections and HPV-induced neoplasms . On the ®  is a vaginal gel that combines components with moisturizing, tissue regeneration, and microbiota-balancing properties  [Coriolus versicolor, Azadirachta indica and carboxymethyl-β-glucan) [® in repairing low-grade cervical lesions induced by HPV in routine clinical practice conditions.In this context, Papilocarecharide) ,19 with -glucan) ,21,22. I-glucan) ,17. Howewww.clinicaltrial.gov (accessed on 12 January 2022); This was an observational, national, multicentric, prospective, non-comparative clinical study performed between May 2018 and February 2021  or low-grade squamous intraepithelial lesion (LSIL) cytology result of, at most, three months prior to the study inclusion together with colposcopy image showing similar level of cervical dysplasia, which was considered concordant with cytology test. To measure the level of satisfaction, visual analogue scale (VAS) was used in patients prescribed Papilocaremponents . Another® one cannula/day for 21 days during the first month, followed by one cannula/alternate days during the subsequent five months. All patients with normal cytology/colposcopy and HPV clearance ended the study after six months (second visit). Those who continued with altered cytology/colposcopy and/or HPV persistency were offered an additional 6-month treatment period with the same dosage, making their total treatment time 12 months (third visit).Participants were treated with PapilocareThis study was approved by the Ethics Committee of Puerta de Hierro Majadahonda University Hospital . All procedures followed were in accordance with the Declaration of Helsinki and subsequent amendments.®. In addition, biopsy evolution was evaluated for those patients who had them available based on routine clinical practice. Improvement in the evolution was described as a change in the histological result in the following order—CIN-3, CIN-2, CIN-1—suggestive of inflammatory HPV and negative. The presence of HPV was measured through a PCR assay. HPV clearance was defined as a negative HPV test or disappearance of all strains present at baseline or disappearance of at least one baseline strain together with normal cytology findings and concordant colposcopy observations. A concordant colposcopy with ASCUS, LSIL, or AGUS cytological results were those findings classified as normal or abnormal in grade 1 (minor) according to the definition from the committee of the International Federation of Cervical Pathology and Colposcopy (IFCPC), defined at the Rio World Congress, 5 July 2011 [The primary endpoint was the repair of the cervical low-grade lesions measured through the percentage of patients with normalized cervical cytology together with concordant colposcopy image. Secondary objectives included the HPV clearance, the level of satisfaction with the product, and the tolerability of Papilocareuly 2011 . RegardiAll data from the clinical study were collected in the CRF; baseline data were gathered during the recruitment visit either via direct question to the patient or during the medical exploration. Data obtained from 6- and 12-month visits were collected in the same way as the baseline data.A sample size of 263 patients provided a ±4.95% precision to identify 80% of patients with their cervical lesions repaired with a 95% confidence interval and assuming 5% losses. Two patient samples were established. The total sample included all patients who met inclusion criteria and with both baseline data and any value corresponding to the primary endpoint. On the other hand, any patient who participated in the study and had at least one application of the investigational product was included in the safety sample.Categorical variables were expressed as absolute and relative frequencies, while continuous variables were represented as mean and standard deviation (SD), 95% confidence interval, median, 25th and 75th percentiles, and minimum and maximum, including the total number of valid values.p ≤ 0.05. All statistical procedures were performed using SAS v9.4.In the case of qualitative variables, comparisons among patient subgroups and visits were performed through the chi-square and McNemar tests, respectively. Additional analyses of both the primary and HPV clearance endpoints were made with data obtained from 3 different subpopulations: patients with HR HPV, patients over 40 years old, and patients over 40 with HR HPV. Statistical significance was established at Out of 263 enrolled patients, 192 and 201 accounted for the total and safety samples, respectively .The mean age in the total sample was 38.7 ± 9.1 years, with 95% of them being Caucasian. Thirty-three patients (17.6%) were vaccinated against HPV, with a 4.3 ± 4.6-year mean time since its administration. Regarding sexual behavior patterns, the participants had a mean number of partners and intercourses within the last month of 1.0 ± 0.5 and 6.3 ± 5.4, respectively. Regarding clinical data, 58.9% and 41.1% of patients had LSIL and ASCUS results in the pap smear, respectively, and 93.2% had HR HPV. Other baseline characteristics are observed in Overall, by the end of the study, the vaginal gel managed to repair cervical lesions in 77.1% of the participants. After 6 months of treatment, 67.0% of the participants had their cervical lesions repaired (95% CI: 60.4–73.7%). Among women that did not repair their lesions in the first 6 months, 58.8% did so by 12 months (95% CI: 42.3–75.4%). The repairing of lesions was achieved in 76% (95% CI: 69.7–82.2%) of patients infected with the HR HPV. At the second and third visits, 66.9% (95% CI: 59.9–73.8%) and 54.8% (95% CI: 37.3–72.4%) of patients had a normalized cytology with a concordant colposcopy image, respectively. The sub-analysis of women over 40 showed that 82.4% (95% CI: 73.8–91.1%) had their lesions repaired overall (74.0% (95% CI: 63.9–84.0%) and 58.3% (95% CI: 30.4–86.2%) in the second and third visits, respectively). Regarding patients over 40 infected with HR HPV, 81.2% (95% CI: 71.9–90.4%) had their lesions repaired overall. At the second and third visits, 73.5% (95% CI: 63.0–84.0%) and 54.5% (95% CI: 25.1–84.0%) reported a normalized cytology with a concordant colposcopy image, respectively .A total of 71.6% of patients achieved HPV clearance by the end of the study. After 6 months of treatment, HPV test results showed 58.7% clearance (95% CI: 51.7–65.8%). Among women that did not achieve HPV clearance in the first 6 months and continued treatment for a further 6 months, 52.1% (95% CI: 38.0–66.2%) achieved it at 12 months. For these patients, data were not available from 30 of them, which includes the patients who did not continue in the study after visit 2 by their own or their physician’s decision, even though they had not cleared HPV (n = 18), and those who were lost/dropped out between visits 2 and 3 (n = 12), from which five were lost during the follow-up visits—three left due to their physician’s decision, one withdrew her consent, and the situations of three were unknown. In participants infected with the HR HPV, HPV clearance was achieved overall by 70.6% (95% CI: 63.9–77.3%) of patients, while 57.4% (95% CI: 50.1–64.7%) and 52.2% (95% CI: 37.7–80.1%) achieved it at 6 and 12 months, respectively. In the case of women over 40, HPV clearance was 75.3% (95% CI: 65.5–85.2%) overall (61.1% (95% CI: 49.9–72.4%) and 57.9% (95% CI: 35.7–80.1%) at the second and third visits, respectively). Regarding patients over 40 infected with HR HPV, 73.5% (95% CI: 63.0–84.0%) of women managed to clear the HPV. At the second and third visits, clearance had occurred in 59.7% (95% CI: 48.0–71.5%) and 55.6% (95% CI: 32.6–78.5%) of patients, respectively .A total of 91 patients had a baseline biopsy taken within 3 months before starting the treatment, of which CIN-1 was the most frequent histological result, representing 61.5% of the analysed cases. Among patients with a biopsy performed at visit 2 (n = 26) and visit 3 (n = 13), 76.9% and 69.3%, respectively, did not show changes in the biopsy results vs. baseline. However, at both follow-up visits, more biopsy improvements than worsening vs. baseline were observed: 15.4% (4 CIN-1 to 3 negative and 1 inflammatory) vs. 7.2% (2 inflammatory to 1 suggestive of HPV and 1 CIN-1) at visit 2 and 23.5% (3 CIN-1 to negative) vs. 7.7% (1 inflammatory to CIN-1) at visit 3 .On a 0 to 10 scale, the satisfaction level with the investigation product remained between 7.5 and 8.0 in both visits. While on the second visit, the mean score was 7.9 ± 1.8, patients rated the product with a 7.5 ± 1.9 on the third visit. In both visits, most of the participants gave a score of 5 out of 10 or above to the product , with 42.4% and 31.1% of women, respectively, giving scores of 9 or higher in each visit . AdherenA total of eight patients reported eight adverse events (AEs), with only three of them considered to be related to the product under investigation. These AEs included two mild and one moderate feeling of vulvovaginal itching/stinging. All of them were resolved by the end of the study.® emerges as an effective and safe option for the treatment of HPV-induced low-grade cervical lesions [Results of this real-life study aligned with previously published data, in which Papilocare lesions ,17. HPV  lesions ,4. Due t lesions . Althoug lesions . In addi lesions ,27,28.®, a Coriolus-versicolor-based vaginal gel which has shown to improve the epithelialization of cervical mucosa and the composition of vaginal microbiota emerges as a safe treatment option for helping to repair low-grade lesions and clear HPV and, additionally, may help lower stress levels in women by making them active participants in their own treatment [p = 0.031). Regarding HR HPV clearance, the superiority of the vaginal gel in the randomized trial was also reported at 6 months, although only a statistical trend was observed (62.5% vs. 40.0%). The results were slightly lower in this real-life study (67.0% lesion repair and 58.7% for HR HPV clearance at six months) although these two studies are difficult to compare due to the different methodological designs. While PALOMA is a clinical trial in which experiments were conducted under controlled conditions, PAPILOBS is a real-world study in which variables are not controlled. However, these studies are complementary, as efficacy and safety found under ideal conditions (such as in the PALOMA study) may substantially differ from those found under real-world settings [® has been addressed by several independent retrospective observational studies [®, the HPV DNA test became negative in 67.0% versus 37.2% of the controls (p < 0.0001). Furthermore, 76.1% versus 40.8% registered a colposcopy improvement (p = 0.0005), and 60.4% versus 40.8% showed a remission (p = 0.05) for treated versus controls, respectively, in 183 HR-HPV-positive patients. Gajino Suárez [In this context, Papilocarereatment ,30. The reatment  in whichsettings . Furthersettings ,33,34. Isettings . In our  studies ,35,36. C studies  showed tThe prospective design with a considerable number of evaluated patients is the main strength of this observational study. On the other hand, limitations included the absence of a control arm and the high number of losses due to the COVID pandemic. In addition, there is a lack of information about both cofactors relevant for the progression of cervical HPV infection to cancer (such as smoking) and covariable-adjusted analyses, which could have avoided potential interferences in the results . The lac® than the spontaneous regression rates reported in the literature. In addition, it has shown an adequate tolerability profile, with high satisfaction and adherence rates. These results are in line with those observed in the PALOMA clinical trial and in other observational studies, which strengthens the role of this vaginal gel as a beneficial alternative to the simple watchful waiting approach in HPV-related low-grade cervical lesions.In our study we observed higher regression rates of low-grade cervical lesions in women treated with the vaginal gel Papilocare


Previous research suggested that the more salient the distractor, the more pronounced is the curvature. We investigate the saliency of spatial visual features, predicted by a constrained maximum entropy model to be optimal or non-optimal information carriers in fast vision, by using them as distractors in a saccadic task. Their effect was compared to that of luminance-based control distractors. Optimal features evoke a larger saccadic curvature compared to non-optimal features, and the magnitude and direction of deviation change as a function of the delay between distractor and saccade onset. Effects were similar to those found with high-luminance versus low-luminance distractors. Therefore, model-predicted information optimal features interfere with target-oriented saccades, following a dynamic attraction–repulsion pattern. This suggests that the visuo-oculomotor system rapidly and automatically processes optimally informative features while programming visually guided eye movements.The saccades’ path is influenced by visual distractors, making their trajectory curve   •Optimally informative visual features interfere with the saccade path to a target•Saliency determined by information maximization is comparable to luminance saliency•Saccade curvature can be used as an objective measure of visual stimuli’s saliency Sensory neuroscience; Cognitive neuroscience Indeed, when a distractor is presented in close spatial proximity to a target, saccades tend to land in between the two objects rather than on the target – the so-called “global effect”.,,,Finally, the effects of a distractor are also reflected in the temporal properties of saccades. Indeed, distractors positioned at a remote location from the target evoke longer saccade latencies as compared to distractors close to a target  weighed by the system processing costs, as proposed by a recent constrained maximum entropy model of early visual feature extraction in fast vision.Here we adopt an operational definition of visual saliency, following the principle that it is proportional to the amount of local information,,,,primal sketch”,sketch were based on a few simple primitives , and were defined a priori based on known properties of visual receptive fields and on the prominent edge detection ability of the visual system. Instead, the aforementioned approach considers computational limitations as a central element together with the information optimization principle to extract a number of visual features from the statistics of natural images, composing a compact saliency map of the visual scene.These authors provided a formal definition of saliency starting from a general problem faced by the visual system: the extraction of biologically relevant information from a large flux of input data in the shortest possible time,pattern matching). Then, it considers the computing limitations of this early stage filter, assuming that there is only a fixed number of visual features that the system can recognize in input (limited storage capacity) and imposing a tight upper bound on the total amount of data that can be produced as output to be transmitted to the next stages of processing (fixed output bandwidth). Such a system is optimal from the point of view of delivering the maximum amount of information to the following processing stages (maximum entropy output), therefore the set of selected features must be information efficient. For a detailed explanation of the exact mathematical definitions of the model function, see.Specifically, the reference model is based on some very general assumptions. First, the model assumes that, at an early stage, the reduction of the huge input data flow is achieved by filtering only those pieces of input data matching a reference set of features, disregarding any other information . High-frequency features that do not fulfill the constrained maximum-entropy optimization criterion are considered non-optimal and should not be used to build the saliency map.The authors then proposed that only optimal features closely resembles the spatial structure of the well-known bar and edge-like receptive fields,,,non-optimal features discarded by the model have either a uniform luminance structure (features with large bandwidth occupation) or a “noisy” alternation of black and white pixels (features with large storage occupation).Model predictions were tested by applying it to a set of natural-scene photographsoptimal features provided highly compressed sketches of visual scenes which resulted in simplified yet informative transformations of the originals, akin to the concept of saliency maps. Indeed, optimal features turned out to be arranged along objects’ contours (edges and lines) rather than being scattered throughout the image. Human participants’ performance in discriminating original images based on very brief presentations (25 ms) of these sketches was very accurate, comparable to that obtained by using their grayscale original versions.In the original study,optimal over non-optimal features even if presented in smaller numbers or at lower contrast,optimal features embedded in images’ fragments contribute to successful image reconstruction.In more recent experiments, we compared the relative saliency of optimal and non-optimal features with psychophysical and eye movement tasks, presenting the features in isolation  for a few milliseconds, without any clues coming from a global structure. We found that observers explicitly prefer Here, we further tested the bottom-up saliency predictions of the constrained maximum-entropy model by using optimal and non-optimal features as task-irrelevant distractors in a saccadic task and measuring the resulting saccadic curvature. This approach allowed us to study the low-level, automatic integration of these optimal features in fast visuo-oculomotor processes, along with its dynamics. We expected that if optimal features are indeed more salient, their presence will interfere in a quick and automatic manner with the ongoing oculomotor programming, and they will induce a larger saccadic curvature. As a control for this saliency effect, we compared it to the saccadic curvature induced by high-luminous versus low-luminous distractors. To characterize the time course of the saliency effect, we investigated changes in saccade trajectory deviations as a function of DSOA. We also looked at the possible effects of distractors’ saliency on endpoint position and saccade latency.—upper panel) or a non-optimal feature ; in the luminance-control condition, the distractor could be a high-luminance  or a low-luminance  feature. In some trials, no distractor was presented, and this condition was used as a baseline. For each normalized saccade , whereas one brief task-irrelevant distractor could randomly appear on the right or on the left (25 ms), halfway from the target  as the relevant variable for the direction of the saccade’s deviation. Previous works on the effect of a distractor on saccadic trajectory have considered the saccadic latency as the independent variable because the target and the distractor were usually presented simultaneously, so latency and DSOA were the same.First, we analyzed saccadic curvature as a function of the DSOA with the SMART procedure , a smootnon-optimal versus optimal features, and for low-luminance versus high-luminance features, are reported in non-optimal and low-luminance features) and slightly negative for the high-salient distractors . All significant DSOA time windows between all types of distractors conditions versus the no-distractor baseline condition are reported in the caption of Saccades time courses for Time courses for each distractor condition have been compared with each other. For each comparison, we report the time window of the significant cluster (ms), the average of smoothed Gaussian data, and the 95% confidence interval within each significant cluster (p < 0.01).optimal features induce a larger deviation away from their location than that induced by non-optimal features (−1.55° versus −0.89° ± 0.2°). At intermediate DSOA  optimal features induce less deviation away than non-optimal (−0.39° versus −0.97° ± 0.2°) and, finally, at short DSOA  they produce a larger deviation toward their position than that induced by non-optimal features (2.05° versus 1.54° ± 0.2°).Comparing the curvature induced by the two types of distractors of the experimental condition A, we obshigh-luminance distractors induce a stronger repulsion than that induced by low-luminance distractors (−1.38° versus −0.8° ± 0.1 and −1.19° versus −0.49° ± 0.2°), whereas, at shorter DSOA , high-luminance distractors induce a stronger attraction than low-luminance distractors (1.10° versus 0.48° ± 0.2°).Similar results emerged in the luminance-control condition B. IndeedOverall, these results indicate that high-salient features, such as very bright features as well as optimally informative features, induce a larger curvature in the saccade trajectory than that induced by lower salient distractors , and this holds both when the curvature is toward and away from the distractor.away is larger for non-optimal versus low-luminance features , as well as for optimal versus high-luminance features . Also, at short DSOA, the deviation toward is larger for non-optimal versus low-luminance features , as for optimal versus high-luminance features . Therefore, both repulsion and attraction effects are more pronounced in the presence of experimental distractors than uniform-luminance distractors.Some differences also emerged by comparing the curvature induced by distractors of the experimental versus control conditions (not reported in the figure). Indeed, at long DSOA, the deviation non-optimal versus optimal features, and low-luminance versus high-luminance features, within two significant time windows found with the SMART procedure, are reported in Examples of average saccades trajectories in the trials with optimal versus non-optimal features cannot be explained by their spatial frequency  with the SMART procedure . The avenon-optimal features  and optimal features . In the same way, with saccade preparation for high-luminance distractors is more delayed than for non-optimal features  and optimal features . This result suggests that saccadic latency is not sensitive to the degree of saliency (either when considering differences in luminance or in amount of information), rather it seems to be influenced by other distractors characteristics. See the No significant differences were detected when comparing the latency time courses obtained with high-salient versus low-salient distractors in both experimental and control conditions. Instead, some differences emerged between the two conditions. Indeed, there are some time windows (ms) where the latency (ms) for low-luminance distractors is shorter than for Finally, the landing point of saccades was also analyzed as a function of DSOA (from −340 ms to −20 ms) with the SMART procedure . SaccadeComparing the landing point time courses obtained with high-salient versus low-salient distractors in both experimental and control conditions, we could observe that the landing position does not change with distractor saliency; neither in the horizontal A nor in We finally analyzed starting X- and Y-positions of saccades (not shown in the figure), finding that they do not differ across distractor conditions nor with DSOA. They are always compatible with the average baseline starting positions  found in the no-distractor condition.,In the present study, we compared the saliency of some local features, originally identified as optimal or non-optimal information carriers based on constrained-entropy maximization criteria.optimal features is larger than that produced by non-optimal ones, suggesting that optimal features act as highly salient distractors which strongly compete with the target location in the oculomotor centers. The results obtained in the luminance-control condition, in which we compared the deviation induced by distractors with high or low luminance contrast, also support our main hypothesis. Indeed, high-luminance features induce a larger deviation than low-luminance features, and the amplitude of the deviation is comparable to that found with optimal features. This suggests that the interference on saccade programming produced by differences in features’ optimality is as powerful as that produced by luminance-based saliency. Note that all types of distractors, also non-optimal and low-luminance features, evoke some degree of curvature compared to the condition where no distractor is presented. This confirms the general finding that the path of target-oriented saccades is influenced by visual competing distractors even if they are task-irrelevant and low visually salient .Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Serena Castellotti (This study did not generate new unique reagents.Comité de Protection de la Personne (CPP: 18.11.07.69231) approved the experimental paradigm, which complied with the Declaration of Helsinki.Twenty-three healthy volunteers participated in the present study . All observers had a normal or corrected-to-normal vision, provided written informed consent prior to their inclusion in the study, and were naive to the purpose of the experiment. The ,,Each participant was tested individually in a dark room. Stimuli presentation was controlled by a MacPro computer (OS 10.6.8), using the Psychophysics Toolbox2; greyscale value = 127). See 2) for a variable duration, uniformly distributed in the range between 400–800 ms, followed by the target  shown for 700 ms at 7° eccentricity on the vertical meridian, above or below the initial fixation. A small distractor stimulus  could randomly appear for 25 ms on the right or on the left, halfway from the target , with a variable delay with respect to target onset (Distractor-to-Target Onset Asynchrony—DTOA from -150 to +50 ms). See At the beginning of the experimental session, a standard nine-point gaze-calibration routine was run and possibly repeated to ensure good quality of the eye movements recordings. The whole task  was presented on a grey display background  and white  pixels, whereas, in the control condition, distractor saliency was based on their luminance contrast with respect to the background.Trials were distributed across three different conditions: baseline , experimental , and luminance-control  condition. Trials’ order was randomized. In the baseline condition, no distractor was presented. The experimental and luminance-control conditions differ for the visual distractor stimuli used in the task: in each condition, the distractor could be high salient  or low salient . In the experimental condition, distractors’ saliency differs based on the spatial arrangement of their black ; whereas the non-optimal feature, used as a “low salient distractor,” was extracted from a set of 50 features selected amongst those with the lowest probability of occurrence in the statistical distribution of all possible features , and thus classified as poorly informative by the model. On average, then optimal and non-optimal features do not differ in their contrast properties. We also assessed the spatial frequency content (SF) of optimal and non-optimal features. The theoretical range of the unidimensional spatial frequency for our 9 x 9 pixels features is between 1.67 and 5 cycles/deg for both sets. In addition, to compare the spatial frequency content of our set of features we computed the distance of the spatial frequency (SF) spectra within and between our two sets of stimuli. This distance is defined as the Euclidian distance of the 9-dimensional vectors of Fourier components’ amplitude, each being a 3x3 two-dimensional spectrum , or a white uniform square with high luminance contrast . The luminance values of the control distractor stimuli were purposely chosen to compare their effect with the effect induced by the distractor used in the experimental condition. Indeed, these values produce a contrast corresponding to the “equivalent contrast” found to match the saliency difference between optimal and non-optimal features.In the luminance-control condition, the distractor stimulus could be a grey uniform square with low luminance contrast with respect to the background . Recorded horizontal and vertical gaze positions were low-pass filtered with a Butterworth  filter of order 2 with a 30 Hz cutoff frequency and then numerically differentiated to obtain velocity measurements. An automatic conjoint acceleration and velocity threshold method was used to detect saccades.For each trial, the first saccade after target onset was analyzed. Trials without recorded saccades , saccades starting further than 2° from the fixation point, saccades landing further than 2° from the target, and saccades with latency lower than 80 ms or longer than 500 ms (with respect to target onset), were excluded .i, yi) was obtained for each bin between the y-position of the fixation and of the target location, removing a possible ambiguity in the definition of the saccadic curvature. Then, we normalized each trace by subtracting the mean trajectory  obtained in the trials without distractors (no-distractor baseline condition), separately for upward and downward saccades. This normalization ensured that any deviation of the saccade trajectory in response to a distractor was not due to the idiosyncratic curvature of the saccade trajectory of individual participants. Normalized y-coordinates were rotated to direct all saccades upward and, finally, normalized x-coordinates were inverted for trials in which distractors were presented counterclockwise  relatively to the target direction. This way, positive and negative values represent coordinates and curvature angles that were directed either toward or away from the distractor’s head-centered position, respectively. With these coordinates we then determined the saccade curvature angle for each trial, that is, the median of the angular deviations of each sample gaze-position point from a straight line connecting the starting and ending point of the saccade.For each correct saccade, we extracted its latency, starting point, landing position, and curvature. To obtain the latter, we first smoothened the gaze position trajectory by computing the mean horizontal position for each 0.2° spatial bin on the vertical axis. By doing this, a unique pair (xFor each saccade, we calculated the DSOA, which is the distractor offset-to-saccade onset asynchrony in milliseconds.p < 0.01. Time courses for each distractor condition have been compared with each other, and each condition has been compared with the baseline condition. Data have been compared in the DSOA window between -340 ms and -20 ms. This interval has been chosen by looking at the distribution of DSOA across all trials (The time course of saccadic curvature, latency, landing point, and starting position as a function of the DSOA has been analyzed with the SMART procedure, a smoothing method for the analysis of response time courses.l trials D and selTo produce figures of average saccade trajectories for each distractor condition C and 2D,


It is expected that interconnected networks of autonomous vehicles, especially during peak traffic, will face congestion challenges. Moreover, the existing literature lacks discussions on integrating next-generation wireless communication technologies into connected vehicular networks. Hence, this paper introduces a tailored bandwidth management algorithm for streaming applications of autonomous vehicle passengers. It leverages cutting-edge 6G wireless technology to create a network with high-speed transmission and broad coverage, ensuring smooth streaming application performance. The key features of bandwidth allocation for diverse streaming applications in this work include bandwidth relay and pre-loading of video clips assisted by vehicle-to-vehicle communication. Through simulations, this research effectively showcases the algorithm’s ability to fulfill the bandwidth needs of diverse streaming applications for autonomous vehicle passengers. Specifically, during periods of peak user bandwidth demand, it notably increases the bandwidth accessible for streaming applications. On average, users experience a substantial 55% improvement in the bandwidth they can access. This validation affirms the viability and promise of the proposed approach in efficiently managing the intricate complexities of bandwidth allocation issues for streaming services within the connected autonomous vehicular networks. In recent years, global car manufacturers have focused on advancing self-driving vehicles. These systems have significantly improved safety and driving convenience. The aim is to achieve fully automated driving in future, where vehicles can independently perceive, evaluate, and respond to various traffic situations, enhancing safety and efficiency. By then, autonomous vehicles will not only be vehicles, but will also become mobile offices and entertainment centers.In the future, it is conceivable that individuals will find themselves with increased opportunities to engage in various activities while in transit, such as handling emails, engaging in video calls, and enjoying high-quality multimedia streaming. However, efficiently allocating bandwidth for the Internet of autonomous vehicles is a challenge. For instance, videotelephony currently consumes a significant amount of bandwidth and is a crucial part of daily life . InsuffiWith the surge in vehicular network traffic, enhancing efficiency becomes paramount. A significant challenge lies in minimizing download delays to enhance the user experience. One viable solution involves the deployment of edge devices for preloading content, a strategy that significantly reduces download times and enhances user satisfaction . AdditioCurrent 5G wireless technology encounters several challenges, including security vulnerabilities, limitations in achieving ultra-low latency, packet loss issues, energy inefficiency, and congestion in high-density areas. These hinder the ability to meet future application demands. Recent literature emphasizes the development of next-generation wireless technologies, as envisioned by experts who aim to achieve advancements in bandwidth, reliability, security, energy efficiency, and ultra-low latency. The overarching objective is to effectively manage a wide range of traffic requirements . SalamehThe terahertz frequency range, which occupies the space between microwave and infrared frequencies, shows promise for achieving low latency, wide bandwidth, and ultra-high-speed data transmission . HoweverWith the continuous growth of future network traffic, the problem of insufficient bandwidth during peak periods becomes a critical issue. There have been numerous research endeavors dedicated to bandwidth allocation within vehicular networks, aiming to adeptly tackle the intricate challenges linked to the fair distribution of network resources among vehicles. Zhang et al.  introducRecent literature has witnessed a proliferation of studies presenting bandwidth allocation solutions tailored to multimedia applications for vehicle users. Several of these studies have incorporated caching techniques to mitigate latency. Furthermore, some have delved into the exploration of Space–Air–Ground-Integrated Networks, a concept that integrates satellite networks and unmanned aerial vehicles to enhance communication capabilities. To name a few, Huang et al.  proposedAcknowledging the vulnerability of conventional centralized control systems to generate computational and transmission bottlenecks, this study introduces a decentralized computational framework aimed at allocating bandwidth in alignment with user demands.The primary contributions and novel aspects of this research can be summarized as follows.This paper utilizes cutting-edge next-generation wireless technologies, including terahertz micro base stations, NLOS-FSOC small base stations with diffuse reflectors, and existing sub-6GHz macro base stations. Collectively, these technologies address passenger bandwidth requirements, establishing a network characterized by low latency, extensive coverage, high-speed connectivity, and exceptional reliability.In our research, we have considered the distinct characteristics of various applications. Specifically, when passengers use real-time applications in situations with limited bandwidth, our algorithm prioritizes allocating the originally designated bandwidth for non-real-time applications to passengers engaged in activities like videotelephony and live streaming. Additionally, we have implemented V2V communication through VLC to enable bandwidth sharing among vehicles within the fleet, allowing for the relay of bandwidth from base stations in other segments. In cases where the required bandwidth remains insufficient, we employ dynamic streaming resolution adjustments, enabling playback with lower bandwidth requirements.In scenarios where there is insufficient bandwidth for non-real-time streaming, this research leverages less congested base stations and autonomous vehicles to assist with the preloading of video segments. When autonomous vehicles converge on the same road segment, VLC is employed for V2V communication to transmit preloaded streaming segments. To determine the arrival times of autonomous vehicles on each road segment, the study utilizes bidirectional long short-term memory (BILSTM)  to estimAs illustrated in Each RSU hosts the “Real-Time Vehicle Speed Calculation” module, which estimates the average vehicle speed as the autonomous vehicle progresses through the managed segments within specific time intervals. It is worth noting that in recent academic research, the Bidirectional Long Short-Term Memory (BILSTM) architecture has garnered significant attention for its effectiveness in predicting vehicle traffic flow across various road segments, as highlighted in previous studies ,30,31,32In cases where streaming service applications are needed during the vehicle’s journey, the OBU’s “Streaming Service Support” module establishes a connection with the streaming service provider. This connection enables the acquisition of the relevant requirements and specifications associated with the application. Guided by the service quality criteria predefined by the autonomous vehicle passenger and assisted by the most up-to-date traffic patterns and base station bandwidth data provided by the RSUs for each road segment, the module dynamically adjusts the bandwidth requirements and quality tailored to the streaming service.Following this, the OBU will communicate the obtained bandwidth requirements for streaming service applications to the RSUs as the autonomous vehicle progresses through various road segments during the usage of these applications. Subsequently, each RSU will allocate the necessary bandwidth for the streaming service application based on its specific demands using the “Real-Time Streaming Bandwidth Allocation” and “Video Clip Preloading” modules. These modules leverage the three categories of base stations mentioned earlier: terahertz micro base stations, NLOS-FSOC small base stations with diffusers, and sub-6GHz macro base stations. These base stations are employed to fulfill the essential bandwidth requirements for the streaming service applications as the autonomous vehicle traverses its managed route segments. In this framework, it is assumed that each RSU maintains a server to record the supply and demand information for streaming applications across distinct time intervals within its managed segments. Consequently, the base stations situated along the route segments will provide the required bandwidth, aligning with the anticipated streaming service quality standards and specifications preset by autonomous vehicle passengers.If the coverage area of a managed route segment’s base station falls short of delivering the required bandwidth, the RSU will step in to ensure that the streaming service quality standards are met, tailoring its approach to the characteristics of the streaming service application. For videotelephony and live streaming, bandwidth allocation can be facilitated through V2V communication with other nearby roadside base stations. Conversely, for on-demand streaming and 360-degree streaming, a video clip preloading mechanism is employed. In scenarios of insufficient bandwidth for on-demand streaming or 360-degree streaming, the RSU examines other autonomous vehicles arriving at the same road segment and time as the streaming-enabled autonomous vehicles. It analyzes their travel routes to identify whether there are base stations capable of providing the required bandwidth for preloading video clips. Upon finding a suitable base station, the managing RSU of that segment coordinates with the supported autonomous vehicles to preload video clips from the base station’s coverage area when they traverse the corresponding road segment. These preloaded video clips are stored in the storage facilities of the designated autonomous vehicles. Consequently, when both the autonomous vehicle equipped with a specific preloaded video clip and the one engaged in streaming reach the same road segment, the V2V communication is employed to transmit the preloaded video clip to the autonomous vehicle passenger who is streaming the content. Furthermore, it is important to note that one of the primary objectives of this research is to evaluate the effectiveness of the pre-loading strategy via V2V communication proposed in this work. Therefore, it is assumed that each required video clip is cached at the base station for access before the conveying autonomous vehicles arrive in the vicinity of the base station to initiate the pre-loading process. Passengers in autonomous vehicles who utilize streaming services also conduct intermittent evaluations of the streaming service quality while it is being used. In situations where modifications to the streaming service quality are deemed necessary, such as reducing it during periods of high demand, the vehicle will establish communication with the “Real-Time Streaming Bandwidth Allocation” or “Video Clip Preloading” module located at the managed Roadside Unit (RSU). This interaction will prompt the respective module to undertake the subsequent task of adjusting the streaming bandwidth as required.The detailed description of each module shown in To predict autonomous vehicle driving speeds, the BILSTM model mentioned earlier is utilized with historical data. This architecture processes information simultaneously in both the forward and backward directions, with the overarching goal of enhancing the accuracy of driving speed predictions for autonomous vehicles.The BILSTM model, as introduced in  and depit, and The conventional LSTM architecture comprises an input layer, a recurrent hidden layer composed of memory blocks as foundational components, and an output layer. Within each memory block, interconnected memory cells preserve temporal states. Moreover, three adaptable multiplication gate cells—the input gate, output gate, and forget gate—oversee the information flow within the block. In the context of the unidirectional LSTM model, it predicts information for a specific future time by utilizing historical data in the following manner:Here, t, and As mentioned above, a BILSTM is employed, consisting of a dual-layered LSTM structure that simultaneously transmits information in both the forward and backward directions. Finally, the two output state variables are concatenated to form the ultimate output. To be specific, the BILSTM takes input sequences E denotes the number of training epochs, L stands for the number of layers, N represents the sequence length, and M is the number of cells per layer [The overall time complexity of training a BILSTM network over a fixed number of epochs is typically O, where |E| and |V| represent the total number of road segments and intersections in this module, respectively.The primary computational overhead in this module occurs during Step 1, which executes the Dijkstra algorithm . The comWhen a passenger in an autonomous vehicle initiates a streaming service, this module initiates a connection with the server of the streaming service provider. The purpose of this connection is to collect the necessary requirements and specifications for the streaming service. Subsequently, the module engages with the RSUs positioned along the vehicle’s route. The primary objective is to assess whether the bandwidth supplied by the base stations, which cover the segments through which the vehicle travels, aligns with the streaming application’s demands. During this evaluation, the module carefully considers the passenger’s predefined quality preferences for their streaming experience. The aim is to ensure that the selected base stations can adequately support the desired streaming quality for the duration of travel along each road segment.In cases of bandwidth insufficiency during peak hours, this module will transmit bandwidth requirement-related information of the autonomous vehicle passenger’s streaming service application to the RSUs positioned along the various road segments traversed during the application’s usage. These RSUs will then, based on the streaming service application’s characteristics and specification demands, undertake bandwidth allocation or preload planning of video segments for the streaming service application as the autonomous vehicle journeys through the managed route segments. Additionally, this module will also respond to bandwidth supply–demand imbalances that arise during peak periods. It will proactively adjust the bandwidth requirements and quality of the streaming service as necessary.Step 1:Upon activation of the streaming service application by the autonomous vehicle passenger, the module interacts with the streaming service provider’s server to acquire the relevant requirements and specifications of the application. Concurrently, this module establishes communication with RSUs positioned along the predetermined route of the autonomous vehicle. The aim is to retrieve information regarding the minimum bandwidth that the base stations can offer as the vehicle traverses through individual road segments.Step 2:In the event that the streaming service application pertains to an on-demand streaming service, the process advances to Step 9 for continued execution.Step 3:Using the duration of the autonomous vehicle’s presence on each road segment and considering the streaming resolution chosen by the passenger, this module computes the dynamic bandwidth needed for each time interval throughout the vehicle’s journey. It subsequently applies the following equations to determine whether the bandwidth from the surrounding base stations meets the necessary criteria for uninterrupted real-time streaming.Step 4:If all the values of Step 5:This component functions in the background mode of execution. Should the itinerary of the autonomous vehicle passenger be altered or if notable deviations occur between the anticipated arrival times at various road segments and the originally projected times, this module reverts to Step 3 to continue execution.Step 6:In cases where the values of Step 7:If the allocation of bandwidth in the preceding step meets the minimum bandwidth requirement for the real-time streaming service, we return to Step 5 and continue the execution. If not, continue to the next step.Step 8:When the base stations along a specific road segment cannot fulfill the real-time streaming service’s minimum bandwidth requirements, and if the streaming service resolution for that segment has already been lowered to the predetermined lower limit, this module informs the autonomous vehicle passenger of the situation. Then, the process returns to Step 5 to continue with further execution.On the other hand, if the streaming service resolution for that segment has not yet reached the predefined lower limit, the module advances to diminish the quality prerequisites for the real-time streaming service of the autonomous vehicle passenger, in accordance with the following equations:Step 9:If the autonomous vehicle passenger’s streaming application involves on-demand streaming or 360-degree streaming, this module calculates whether the autonomous vehicle can smoothly play each video clip of the streaming service during its journey. This means that the video clips can be preloaded to the onboard storage facilities of the vehicle before their playback. This module evaluates whether the base stations along the route can meet the requirements for preloading each video clip of the streaming service using the following equations:s. s, respectively. In this context, Step 10:In scenarios where the calculated bandwidth for any video clip, whether intended for on-demand streaming or 360-degree streaming, falls below the minimum required download bandwidth threshold as the video clip traverses a road segment, this module activates an alert targeted at the supervising RSU of the corresponding road segment. The objective of this alert is to foster synchronization among other autonomous vehicles, prompting them to preload the essential video clips essential to the streaming service. Upon receipt of this alert, the overseeing RSU initiates the “Video Clip Preloading” module. This module’s role is to orchestrate other autonomous vehicles, ensuring the preloading of the requisite video clips. This guarantees their availability prior to the commencement of the streaming service playback. The autonomous vehicle utilizing the streaming service is also informed about the status of these preloaded video clips. Step 11:If the preload arrangements made in the previous step do not adequately cater to the playback demands of all video clips, proceed to Step 13 for further execution.Step 12:This module operates in a background execution mode. If there are any alterations to the autonomous vehicle passenger’s route or substantial deviations in the estimated time of arrival at different route segments, the process should revert back to Step 9 for further execution.Step 13:If the streaming service resolution has been lowered to the system-defined lower limit, this module notifies the autonomous vehicle passenger and returns to Step 12 to continue execution.Step 14:Decrease the resolution of the streaming service according to the following equation:The steps executed by this module are as follows:The computational overhead in this module is primarily driven by the real-time bandwidth allocation or preload planning of video segments for the streaming service application, based on the characteristics and specification demands of the streaming service. Since Step 6 and Step 10 in this module respectively invoke the “Real-Time Streaming Bandwidth Allocation” and “Video Clip Preloading” modules to perform the aforementioned tasks, the computational overhead is determined by the maximum execution time of these two modules. Furthermore, this process may be iterated for different resolution levels, as indicated in Step 14. Consequently, the complexity of this module can be estimated as the maximum execution time required by the two modules mentioned above, multiplied by the number of resolution levels.When the RSU receives bandwidth requests for real-time streaming applications from autonomous vehicle passengers with scheduled arrivals at its managed segment, it initiates a preliminary evaluation. This assessment is conducted to verify whether the bandwidth provided by the base stations along the traversed segment is sufficient to meet the requirements of the streaming service. Should the available bandwidth upon the autonomous vehicle’s arrival at the managed RSU segment prove inadequate for the streaming service requirements, this module engages in a reallocation process. It reassigns the previously designated bandwidth, which was initially allocated for concurrent on-demand streaming or 360-degree streaming preloads occurring during the same timeframe. This reallocation is aimed at accommodating the exigencies of the real-time streaming requirement.If, despite the mentioned bandwidth reassignment, the requirement for real-time streaming bandwidth remains unmet, the RSU employs an alternative approach. It establishes connections between the autonomous vehicle and other vehicles that have arrived at the same segment and are within the range of V2V communication. Through the establishment of a cooperative vehicle fleet, the final vehicle within the fleet acquires sufficient bandwidth from the base stations capable of meeting the streaming service requirements. Subsequently, the harvested bandwidth is transmitted from the last vehicle in the fleet to the requested vehicle via V2V communication. This collaborative strategy guarantees that the requested autonomous vehicle receives the essential bandwidth to effectively support the streaming service for its passenger, thereby enhancing the overall streaming experience.Step 1:This module gives precedence to distributing the remaining bandwidth of base stations within the coverage area of the autonomous vehicle’s route to the specific real-time streaming service. If the service is a videotelephony, the module also allocates any unused upload bandwidth of the base stations to facilitate that videotelephony. If the available base station bandwidth is sufficient to fulfill the real-time streaming service’s bandwidth requirement, the module completes its execution. If not, it moves to the next step.Step 2:This module reallocates the originally designated bandwidth, which is initially intended for other on-demand streaming or 360-degree streaming preloads occurring within the same time frame, to address the bandwidth requirements of the essential real-time streaming.Step 3:If the revised allocation effectively satisfies the bandwidth prerequisites of the real-time streaming service, this module advances to Step 6. However, if the adjusted allocation falls short of fulfilling the requirements, this module proceeds to the subsequent step in the sequence.Step 4:f-th vehicle within the established fleet. When the bandwidth needed for the real-time streaming service remains insufficient, the RSU communicates with any autonomous vehicle traversing the same road segment within the designated time frame. Through established connections between the vehicle utilizing the streaming service application and other autonomous vehicles, this module utilizes the equations listed below to identify base stations capable of providing the required bandwidth for the real-time streaming service. By harnessing the interconnections within this fleet of vehicles, the essential bandwidth is relayed from base stations possessing sufficient bandwidth capabilities to the passenger’s autonomous vehicle using V2V communication. This collaborative strategy facilitates the delivery of the required bandwidth for the real-time streaming service.Step 5:The results of the bandwidth relay are sent back to the initiating autonomous vehicle and the execution of this module is finalized.Step 6:If the allocation of bandwidth from the base stations to real-time streaming within the same time slot results in video clips for other on-demand streaming or 360-degree streaming being unable to be preloaded promptly, this module triggers the “Video Clip Preloading” module. This activation aims to aid in meeting the preloading prerequisites for the pertinent supported video clips.The steps executed by this module are as follows.The main computational burden in this module arises during Step 4, which involves organizing a fleet of vehicles to transfer essential bandwidth to the passenger’s vehicle through V2V communication from base stations equipped with sufficient bandwidth capabilities. Since bandwidth is relayed from base stations located in the less congested outlying areas of the metropolitan region to autonomous vehicles navigating congested road segments in the central metropolitan region, the maximum distance for bandwidth relay along a path is determined by the longest distance a vehicle travels between the two farthest points in the outlying areas of the metropolitan region.When confronted with limited bandwidth for on-demand streaming or 360-degree streaming, the RSU initially assesses the road segments along the route traversed by the streaming-enabled autonomous vehicle before the occurrence of the bandwidth deficiency. It explores the presence of base stations within the autonomous vehicle’s route coverage area capable of allocating the necessary bandwidth for preloading the required video clips. In situations where the base stations along the route cannot provide the necessary bandwidth, the system turns to other autonomous vehicles that are passing through the same road segments within the streaming service’s operational timeframe. The objective is to identify the base stations capable of providing the necessary capacity for preloading the essential video clips. Once a suitable base station is pinpointed, the RSU selects an autonomous vehicle capable of accessing the coverage range provided by that specific base station. This chosen vehicle is then tasked with preloading the required video clips from the identified base station. The preloaded video clip is subsequently stored in the onboard storage of the autonomous vehicle responsible for the preloading. As a result, when the autonomous vehicle that performed the preloading and the autonomous vehicle requesting the video clip come into proximity, the preloaded video clip is sent to the inquiring vehicle via V2V communication.Step 1:Recognizing that the base stations along the route segment of the autonomous vehicle are insufficient to meet the playback requirements for video clip requested by the vehicle’s passenger, the RSU initiates an investigation into road segments the autonomous vehicle has previously passed through. The aim is to identify base stations within the coverage area that possess sufficient bandwidth to facilitate the preloading of essential video clips for the passenger of the autonomous vehicle.Step 2:If any base stations capable of furnishing adequate bandwidth for preloading the required video clip before playback are identified along the vehicle’s route, the relevant RSU overseeing that specific route segment is informed, and the execution of this module comes to an end. If no such base stations are located, then we advance to the subsequent step.Step 3:ρ represents the supporting autonomous vehicle for clip preloading, and ρ travels along the road segment covered by base station ρ to preload the video clip.On the route of autonomous vehicle Step 4:We send the outcomes of the preload arrangement from the preceding step back to the autonomous vehicle that initiated the bandwidth request and finalize the execution of this module.The following steps are executed by this module:The primary computational overhead in this module arises in Step 1 and Step 3, where the RSU endeavors to locate a suitable base station for the preloading of video clips needed for on-demand streaming or 360-degree streaming. Its complexity is calculated by multiplying the number of vehicles simultaneously reaching the same road segment by the number of road segments they may traverse along the longest possible route.This study conducted a series of simulations to evaluate the effectiveness of the proposed algorithm. The simulations were performed on a personal computer equipped with an Intel Core i9 5.2 GHz CPU and 128 GB of RAM. The simulation code was developed using Python. In our simulation, traffic density data from a website providing information about traffic in New York City  was usedThe foundation of this study relies on categorizing the multimedia streaming applications used by passengers in autonomous vehicles into four types: videotelephony, live streaming, 360-degree streaming, and on-demand streaming. As the vehicles were in motion, the streaming applications requested by autonomous vehicle users were initiated randomly. The counts and ratios for various applications were determined through an analysis of the four types of streaming applications documented in ,42,43,44The hyperparameters utilized in the proposed algorithm are listed below. For the BILSTM model, following the methodology outlined in , we confWithin the simulated region, an array of base station categories was deployed, including terahertz , NLOS-FSRemarkably, on-demand streaming remains the preferred choice for passengers due to its flexibility in viewing schedules, content replayability, and wide range of content options. This preference holds true during both peak and off-peak hours. In contrast, the availability of 360-degree streaming services is relatively limited. This scarcity is not only due to the limited prevalence of devices capable of viewing 360-degree streaming but also because the quantity of 360-degree streaming content available on streaming platforms is far from matching the abundance of on-demand streaming content.As shown in Regarding the bandwidth requirements of videotelephony and live streaming, In scenarios where the bandwidth requirements of streaming applications for autonomous vehicle passengers cannot be fully met, the proposed “Streaming Service Support” module comes into play. This module dynamically adjusts the bandwidth requirements and quality of the streaming service to address disparities between the supply and demand for bandwidth, particularly during peak usage times. Additionally, the “Real-time Streaming Bandwidth Allocation” module prioritizes the redistribution of the initially allocated bandwidth, originally designated for non-real-time applications, to support real-time videotelephony and live streaming demands. Alternatively, through V2V communication, the bandwidth supplied by base stations located on other road segments can be relayed to facilitate seamless videotelephony and live streaming for users. Specifically, after implementing the proposed algorithm, the bandwidth for real-time videotelephony and live streaming applications improves by 73% and 60%, respectively, during the high-demand periods from 7:00 a.m. to 10:00 p.m.Regarding on-demand streaming and 360-degree streaming, the “Video Clip Preloading” module is employed to preload video clips for autonomous vehicle passengers before their playback times. In cases where these vehicles converge on the same road segment as the supported autonomous vehicles facing bandwidth limitations for on-demand and 360-degree streaming, video clips can be preloaded to the conveying vehicles within the coverage of base stations with sufficient bandwidth. The preloaded video clips can then be transmitted to the requesting autonomous vehicle passengers for playback through V2V communication. Consequently, bandwidth availability for on-demand and 360-degree streaming sees improvements, with increases of 58% and 57%, respectively, during the high-demand periods from 7:00 a.m. to 10:00 p.m. This improvement underscores the effectiveness of this work in prioritizing real-time applications and optimizing non-real-time streaming applications through the proposed preloading strategies.During the period from 7:00 a.m. to 10:00 p.m., when the base stations on congested road segments had reached their maximum bandwidth capacity, all passengers in autonomous vehicles experienced an average bandwidth improvement of 55%. Furthermore, throughout the two peak demand intervals, in the morning at 8:00 a.m. and in the evening at 7:00 p.m., passengers in autonomous vehicles saw remarkable increases of 90% and 100%, respectively, in the available bandwidth.This study employs a decentralized computational architecture to address the computational complexities associated with traditional centralized control frameworks. The total computational complexity of the proposed work can be divided into three distinct parts. Firstly, the “Real-Time Vehicle Speed Calculation” module at a RSU continually updates the average vehicle speed as the autonomous vehicle travels through managed segments within specific time intervals. When an autonomous vehicle begins its journey, it utilizes the “Real-Time Driving Speed and Time Calculation” module to determine its driving route.Following this, when a passenger initiates a streaming service application within the autonomous vehicle, the remaining three modules collaborate to allocate the necessary bandwidth for the streaming service. Specifically, the “Streaming Service Support” module invokes the “Real-Time Streaming Bandwidth Allocation” and “Video Clip Preloading” modules to perform real-time bandwidth allocation or preload planning of video segments for the streaming service application, based on the characteristics and specification demands of the streaming service. Therefore, the maximum computational complexity in this context should be determined as outlined in the last paragraph of the “Streaming Service Support” module.It is noteworthy that this study introduces an innovative approach to address the potential bandwidth limitations faced by upcoming vehicular networks, especially during periods of high demand. The proposed approach involves a unique strategy: by accurately calculating the estimated arrival times of autonomous vehicles at different segments of their routes, and by leveraging V2V communication, it becomes possible to efficiently distribute bandwidth resources. This redistribution of bandwidth occurs from base stations strategically positioned in less congested road sections to the autonomous vehicles that require additional bandwidth support. To be specific, the proposed algorithm effectively facilitates the delivery of preloaded video clips to autonomous vehicles facing bandwidth inadequacies while navigating congested roads, utilizing V2V communication. This results in a substantial increase in the allocated bandwidth for streaming applications used by autonomous vehicle passengers. Furthermore, it substantially narrows the gap between the acquired and required bandwidth. Last but not least, the proposed algorithm further boosts overall bandwidth utilization by employing bandwidth relay from less congested base stations.While the existing literature has laid the foundation for bandwidth allocation algorithms within vehicular networks, there is still an opportunity to explore the untapped potential presented by the emerging next-generation wireless communication technologies within the dynamic context of Autonomous Vehicle Networks. This study is driven by the mission to bridge this gap by seamlessly integrating cutting-edge terahertz and NLOS-FSOC wireless communication technologies with the more conventional sub-6 GHz band base stations. The overarching goal is to ingeniously distribute the necessary bandwidth for streaming applications, catering with precision to the passengers of autonomous vehicles. While the existing literature has paved the way for bandwidth allocation algorithms in vehicular networks, a noticeable void persists in harnessing the untapped potential of emerging 6G wireless communication technologies within the intricate landscape of the Internet of Autonomous Vehicles. This study is dedicated to closing this gap by seamlessly integrating state-of-the-art terahertz and NLOS-FSOC wireless communication technologies with the established sub-6 GHz band base stations. The overarching objective is to strategically allocate the necessary bandwidth for streaming applications, tailored to meet the discerning needs of passengers within autonomous vehicles. To materialize this objective, the study adeptly deploys terahertz and NLOS-FSOC technologies, orchestrating a harmonious coexistence of micro and macro base stations alongside the well-established sub-6 GHz band base stations. An outstanding addition is the strategic incorporation of BILSTM, which serves as the linchpin for the timely and precise allocation of bandwidth. This allocation is intricately aligned with the distinct quality requirements of individual passengers as they embark on their autonomous journeys. Elevating the innovative landscape, the study introduces VLC technology, a pivotal enhancer of both security and data rates in V2V communications. This augmentation significantly raises the bar for the quality of V2V streaming transmissions, consequently elevating the holistic streaming experience. The effectiveness of these proposed mechanisms is demonstrated through a comprehensive analysis of the simulation results. The simulation results reveal that the proposed work significantly enhances the available bandwidth for autonomous vehicle passengers during periods when base stations on congested road segments have reached their maximum bandwidth capacity. The results indicate an average bandwidth improvement of 55%, which is accessible to all passengers in autonomous vehicles. As a result, a wide range of streaming applications—encompassing videotelephony, live streaming, on-demand content, and immersive 360-degree experiences—can seamlessly access timely and essential bandwidth support. This holds true even when base station resources face strain during heightened travel demands. In conclusion, the innovative algorithm presented in this study goes beyond mitigating potential bandwidth conflicts among passengers within autonomous vehicles for base station resources. It ensures uninterrupted access to streaming applications, especially during peak demand periods. This comprehensive approach systematically maximizes overall service satisfaction by skillfully addressing the diverse quality needs of the majority of passengers. In doing so, it firmly positions itself at the forefront of bandwidth management within the evolving realm of the Internet of autonomous vehicles. Limited by the dataset availability for autonomous vehicles, we have used currently available traffic data of vehicles in our simulations. However, in the future, when real-world datasets of autonomous vehicles become accessible, we intend to employ up-to-date autonomous vehicle datasets for our simulations in future research. Additionally, we plan to broaden the scope of our research to encompass both human-driven and autonomous vehicles within vehicular networks. We aim to develop strategies to address the challenges associated with predicting vehicle travel times in the complex landscape of hybrid vehicular networks, which include both human-driven and autonomous vehicles.Furthermore, we plan to explore the potential utilization of additional technologies, such as satellites and drones, as sources of bandwidth in the future. Lastly, we are committed to integrating state-of-the-art machine learning predictive streaming content caching systems into our future research endeavors to enhance the overall comprehensiveness and effectiveness of our systems.