A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> V. SUMMARY AND OUTLOOK <s> Vehicle speed trajectory and traffic signals significantly impact vehicle’s fuel consumption and travel time in the urban road network. In this paper, we proposed an optimal vehicle routing algorithm that takes the waiting time at signalized intersections and eco-driving model into consideration. First, the data of the floating car collected by the GPS are matched with the electronic map, and the average traveling speed of the vehicle in each road segment is calculated in real time. The position and timing information of the signal lights at each intersection are pre-acquired to establish an eco-driving model at signalized intersections with the support of cooperative vehicle infrastructure system technologies. The vehicle accelerates through the signalized intersection to reduce the waiting time in the case, where the headway is allowed, while the vehicle decelerates to the minimum speed to avoid idling. Based on the traffic lights red light, green light conversion probability, and vehicle energy-saving driving model, the signal light cycle duration is divided into four parts: the green light pass section, the red-light acceleration section, the red light idle section, and the red-light deceleration section. Combining the probability distribution and the fuel consumption model, the average fuel consumption at the intersection area can be calculated. Taking the optimal energy consumption as the goal, combined with the A* algorithm, an optimal path algorithm that considers the influence of traffic lights and energy consumption is proposed. Finally, two examples are tested, including a real-world road map in Changsha city to demonstrate the effectiveness of the proposed algorithm. And the results show that the proposed model has good performance on energy consumption reduction. <s> BIB001 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> V. SUMMARY AND OUTLOOK <s> Objective Hearing loss caused by high levels of noise is a potential occupational health disorder among train drivers around the world. This study aims to investigate the relationship between tunnel driving occupational environment and hearing loss in train drivers, to provide some insights into helping reduce hearing loss among train drivers. Methods This study analysed cross-sectional data for 1214 train drivers who work at China Railway Guangzhou Group. Health examination was taken by physicians with professional licences, and audiometric testing was performed by health technicians in a sound-isolated room. T/R is defined as the ratio of the length of the tunnels to the length of the railway along drivers’ work routes. Different multivariate models and stratified models were established for sensitivity analysis. A multivariable logistic regression model was used to estimate the ORs of hearing loss associated with tunnel driving occupational environment. Results The adjusted OR for high-frequency hearing loss in association with the highest T/R levels (30%–45%) versus the lowest T/R levels ( Conclusions This study found that there was a significant association between tunnel driving occupational environment and hearing loss. Train drivers who work in a higher T/R environment have worse hearing loss. <s> BIB002
At present, the research of traffic conflicts based on intelligent vehicle relies on the technology of environment perception, including wireless communication, sensor measurement, and the V2X technology. The wireless communication and sensor measurement can be used to obtain the road information for quickly determining or predicting abnormal traffic conditions on the road. The V2X technology are employed to achieve the cooperation among vehicles, pedestrians and environment. The goal of optimizing the usage of system resources can be achieved to improve the road safety and alleviate the traffic congestion. On this basis, intelligent vehicle can achieve optimal trajectory planning and motion control to reduce conflicts with other road users. The framework of the research of intelligent vehicle traffic conflict is shown in Fig. 12 . According to the different sensors used in the intelligent vehicles, they are divided into two categories: one in which the environment perception is based on monocular vision, stereo vision, lidar and other single sensor processing technologies; The other one in which the environment perception is based on multi-source sensor fusion technology. Due to the sensor bias or measurement noise, measurement data may be missing or wrong. Therefore, how to meet the strict reliability requirements of environmental perception using sensor data with limited accuracy is one of the most challenging problems. In order to obtain reliable and accurate data, high-quality sensors can be used, but this will lead to the problem of high cost. Meanwhile, it is difficult for a single sensor to cope with the complex and varying driving environment. At present, most of the intelligent vehicles realize accurate and efficient environmental perception via sensor fusion. In the predictable future, the improvement of intelligent transportation system and sensor technique will improve the accuracy of the sensing data. V2X communication technology combines vehicle terminal and intelligent roadside perception to realize the functions of early warning, traffic congestion prediction, active safety assessment and driving behavior analysis in accident-prone sections. Location technology is dominant, and the existing high-precision positioning technology is still in the stage of continuous improvement, so that high-precision sensors have certain advantages in achieving the accuracy application. However, the sensor technique has no good solutions in obtaining traffic information in the blind areas of intersections. To solve this problem, V2X technology has made up for the defect and embodied strong complementary advantages. It can judge the position of vehicles relative to other vehicles, intersections and infrastructure. However, there are still some problems to be solved before V2X technology can be deployed on a large scale. On the one hand, due to the high mobility of vehicles, the influence of communication transmission distance, and the transmission delay and the packet loss, only under certain precision conditions can the intelligent vehicles grasp the surrounding traffic situation comprehensively. There are security risks for existing communications to prevent information from leaking or being attacked by the hackers. Therefore, it is necessary to overcome the technical difficulties of information security. With the tremendous advantages of deep learning method in the field of environmental awareness, the combination of perception-communication fusion technology is the mainstream solution of traffic conflicts. For intelligent vehicles, the technical difficulties that need to be solved include the fusion of environmental data, the vehicle trajectory tracking, the active collision avoidance, the improvement of positioning accuracy and communication accuracy, the route planning and driving comfort BIB001 - BIB002 , etc. In future researches, emerging technologies toward a connected vehicle-infrastructure-pedestrian environment with big data have made it easier and cheaper to collect, store, analyze, use, and disseminate multi-source data. The connected environment also introduces new approaches for flexible control and management the transportation systems in real time to improve the overall system performance. Considering the highly mixed environment of intersections with trajectory conflict and traffic competition restriction, as well as the severe congestion and frequent traffic accidents, the researches on traffic conflicts based on intelligent vehicles will tend to be active safety. Collaborative control is a feasible solution to intersection problems. The framework of cooperative control is shown in Fig. 13 . Aiming at detecting the vehicles before conflicts occur, potential conflicts are then resolved by alternative driving strategies. The realization of collaborative control relies on the vehicle network. The real-time communication function is utilized in vehicle-infrastructure collaborative environment, which collects and analyzes the vehicle information near the intersection. The signal timing is optimized to guide the vehicle speed to a certain extent. The release plan for the vehicle is distributed directly by the roadside unit. In addition, the autonomous driving is expected to revolutionize the road traffic by attenuating current externalities, especially accidents and congestion. The remaining doubts and challenges are still huge to overcome. The implementation of the autonomous driving environment encompasses not only complex automotive technology, but also human behavior, ethics, traffic management strategies, policies, liability, etc. Legal issues have already been arisen in the context of highly-automated driving. They range from the needs for special driving licenses to much more intricate topics like liability in the event of an accident or privacy issues. All these legal and ethical concerns could hinder the spread of autonomous vehicles once technologically feasible.