A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> Pedestrian safety is a primary traffic issue in urban environment. This article deals with the detection of pedestrians by means of a laser sensor. This sensor, placed on the front of a vehicle collects information about distance distributed according to 4 laser planes. Like a vehicle, a pedestrian constitutes in the vehicle environment an obstacle which must be detected, located, then identified and tracked if necessary. In order to improve the robustness of pedestrian detection using a single laser sensor we propose here a detection system based on the fusion of information located in the 4 laser planes. In this paper, we propose a Parzen kernel method that allows first to isolate the ldquopedestrian objectsrdquo in each plane and then to carry out a decentralized fusion according to the 4 laser planes. Finally, to improve our pedestrian detection algorithm we use a MCMC based PF method allowing a closer observation of pedestrian random movement dynamics. Many experimental results validate and show the relevance of our pedestrian detection algorithm in regard to a method using only a single-row laser-range scanner. <s> BIB001 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> Rear-end collisions account for a substantial amount of crashes. The vast majority of rear-end collisions occur at speeds up to 30 km/h, mostly in city traffic. A common cause of these crashes is driver distraction. A rear-end collision might lead to soft-tissue neck injuries for the occupants in both vehicles involved, as well as material damages. The objective of this study is to present and discuss the potential benefit of a production system helping the driver to mitigate and in certain situations avoid rear-end collisions in low speed. City Safety monitors the traffic in front with the help of a laser sensor that is built into the windscreen's upper section. It can detect the rear-end of a vehicle in front of the City Safety equipped car. If the driver is about to drive into the vehicle in front and does not react in time, the car brakes itself. The scope for the system is every day low speed scenarios, like queues or entering roundabouts, situations where a large portion of collisions appear due to distracted drivers. City Safety is active at speeds up to 30 km/h. If the relative speed difference between the vehicles is less than 15 km/h it can help the driver to avoid a collision completely. In relative speed differences above 15 km/h up to an absolute speed of 30 km/h the objective is to reduce speed as much as possible before a collision occurs. Independent evaluation has shown that this technology offers the potential benefits of reducing collisions, leading to a substantial reduction in car damage costs and injuries to the occupants. Based on available statistics and dose-response model techniques, the reduction of impact severity is estimated to have the potential to reduce the risk of soft-tissue neck injuries in the rear-end impacted car by approximately 60%. Real-world retrospective studies of the production system will enable more precise quantification of the effect in the future. The full text of this paper may be found at: http://www-nrd.nhtsa.dot.gov/pdf/esv/esv21/09-0371.pdf For the covering abstract see ITRD E145407. <s> BIB002 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> Given a series of point sets sampled from a rigid surface by a 3-D rangefinder, we study the problem of estimating the motion and surface structure of a dynamic object. This target tracking problem with 3-D data can be formulated as maximizing the likelihood of the data (the scan map) and the Gaussian mixture model (GMM; object model up to the previous time step). We choose the prior for the object model from the conjugate distribution family of the GMM to yield a trackable posterior distribution for the object model. This GMM-based nonparametric model can be indexed by a hash lookup table, and we show that the method's complexity linearly scales with the number of scan points. Quantitative performance evaluation demonstrates that the proposed method substantially outperforms others. Results of road tests in divided freeway and urban scenes show the accuracy and robustness of the system, which can enable many vehicle active-safety and driver-assistance applications. <s> BIB003 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> Sensors have been developed and applied in a wide range of fields such as robotics and autonomous vehicle navigation (AVN). Due to the inability of a single sensor to fully sense its surroundings, multiple sensors based on individual specialties are commonly used in order to complement the shortcomings and enrich perception. However, it is challenging to integrate the heterogeneous types of sensory information and produce useful results. This research aims to achieve a high degree of accuracy with a minimum false-positive and false-negative rate for the sake of reliability and safety. This paper introduces a likelihood-based data fusion model, which integrates information from various sensors, maps it into the integrated data space and generates the solution considering all the information from the sensors. Two distinct sensors: an optical camera and a LIght Detection And Range (Lidar) sensor were used for the experiment. The experimental results showed the usefulness of the proposed model in comparison with single sensor outcomes. <s> BIB004 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> We propose an obstacle classification method using multi feature comparison based on 2D LIDAR. The existing obstacle classification method based on 2D LIDAR has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classify obstacle type, and therefore accurate path planning was not possible. To overcome this problem, a method of classifying obstacle type based on width data had been proposed. However, width data was not sufficient to enable accurate obstacle classification. The proposed algorithm of this paper involves comparison with database to classify obstacle type. Database was generated using width, intensity and range variance data. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D LIDAR-based method, thus demonstrating the possibility of obstacle type classification using single 2D LIDAR. <s> BIB005 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> This paper studies the pedestrian recognition and tracking problem for autonomous vehicles using a 3D LiDAR, a classifier trained by SVM (Support Vector Machine) is used to recognize pedestrians, the recognition performance is further improved with the aid of tracking results. By comparing positions and velocity directions of pedestrians with curb information, alarms will be generated if pedestrians are detected to be on road or close to curbs. The proposed approach has been verified on an autonomous vehicle platform. Curb information is used to divide pedestrian candidates into two classes, i.e., on-road and out of road.Tracking aided recognition strategy is used to improve the true positive rate, for example, for those on-road candidates, who have been recognized as pedestrians in former frame are classified to be pedestrians directly.This may increase the true positive rate for candidates who become too close or too far from moving autonomous vehicle in the following frames, where only a few laser beams are irradiated on pedestrians.Hash table is used for searching and comparison in the segmentation procedure to increase the efficiency of the proposed algorithm.The pedestrian recognition and tracking system is integrated with the autonomous vehicle platform which provides timely prediction of pedestrian motions. <s> BIB006 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> Abstract The transportation methodologies followed nowadays have evolved a lot with the implementation of embedded features in it. With the trends in today's autonomous vehicles driven by batteries with electric drives (EVs), the present paper deals with one of the key features of autonomous vehicle, Fuzzy based Lidar sensor model. The present paper focus on the sensing features of LiDAR sensor with fuzzy based control model with respect to movement of the vehicle with the parameters like braking and throttle values referred to distance of the object from the vehicle. The model describes the complete analysis on fuzzy based Lidar sensor in forward direction of the vehicle by developing control loop, fuzzy rules and validating the loop using MATLAB. fis model, python, processing code with arduino environment. <s> BIB007 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> In this study, we developed an autonomous driving system for mountainous public roads. Three-dimensional normal distribution transform (NDT) scan matching is employed for localization and a model predictive controller is utilized for vehicle motion control. In order to increase the robustness of the localization method, the estimated poses are computed by an extended Kalman filter using dead reckoning and NDT information. The uncertainty of the pose estimated by NDT is determined by using the Hessian matrix computed in the optimization process for scan matching. We conducted experiments in a public road environment over 20 times and all of the tests were successful. The experimental results confirmed that the autonomous driving system can operate reliably in mountainous public roads. In addition, the evaluation results obtained for the localization method showed that accurate and robust localization can be achieved in mountainous rural environments. <s> BIB008 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as cars, pedestrians and cyclists. We formulate this problem as a point- wise classification problem, and propose an end-to-end pipeline called SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a transformed LiDAR point cloud as input and directly outputs a point-wise label map, which is then refined by a conditional random field (CRF) implemented as a recurrent layer. Instance-level labels are then obtained by conventional clustering algorithms. Our CNN model is trained on LiDAR point clouds from the KITTI dataset, and our point-wise segmentation labels are derived from 3D bounding boxes from KITTI. To obtain extra training data, we built a LiDAR simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize large amounts of realistic training data. Our experiments show that SqueezeSeg achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per frame), highly desirable for autonomous driving applications. Furthermore, additionally training on synthesized data boosts validation accuracy on real-world data. Our source code and synthesized data will be open-sourced. <s> BIB009 </s> A Review of Research on Traffic Conflicts Based on Intelligent Vehicles <s> B. RESEARCH ON TRAFFIC CONFLICT BASED ON RADAR <s> Abstract Light Detection and Ranging (LiDAR) is a remote sensing technology widely used in many areas ranging from making precise medical equipment to creating accurate elevation maps of farmlands. In transportation, although it has been used to assist some design and planning works, the application has been predominantly focused on autonomous vehicles, regardless of its great potential in precise detection and tracking of all road users if implemented in the field. This paper explores fundamental concepts, solution algorithms, and application guidance associated with using infrastructure-based LiDAR sensors to accurately detect and track pedestrians and vehicles at intersections. Based on LiDAR data collected in the field, investigations were conducted in the order of background filtering, object clustering, pedestrian and vehicle classification, and tracking. The results of the analysis include accurate and real-time information of the presence, position, velocity, and direction of pedestrians and vehicles. By studying the data from infrastructure-mounted LiDAR sensors at intersections, this paper offers insights into some critical techniques that are valuable to both researchers and practitioners toward field implementation of LiDAR sensors. <s> BIB010
In addition to the usage of machine vision and image processing technology, the environment-aware technology of intelligent vehicles also has peripheral obstacle detection approaches using radar (laser, millimeter wave, ultrasonic). An example of intelligent vehicles radar sensor is shown in Fig. 6 . Compared with the other sensors such as millimeter wave radar, laser radar has the advantages of high resolution and good recognition effect, and has become a more and more mainstream sensor for autonomous driving vehicles  . Jo et al. BIB004 studied that lidar sensors do not require external light to sense, and can achieve obstacles in low light and nighttime detection, overcoming the lighting conditions. An example of the usage of lidar is the Volvo City Safety AEB system BIB002 : When an impending collision is detected, the system monitors the threatened vehicle ahead with the lidar sensor and brakes without driver intervention. The perceptual tasks based on lidar have attracted a lot of research attention, including point cloud clustering, passable area analysis, obstacle detection and dynamic obstacle tracking  . Lee et al. BIB005 combined distance thresholds with the return order of point cloud data for single-line lidar detection of pedestrians, bicycles, rubber cones and cars. Gidel et al. BIB001 minimized the distance between each scanning point and the fitted line in the case of a pre-known unknown point cloud structure, in order to achieve the 4-line laser point cloud clustering, effectively segmenting vehicles and pedestrians. Zeng et al. BIB003 proposed the maximum likelihood estimation as the data association algorithm, and proposed a tracking algorithm based on Gaussian mixture model and extended Kalman filter to achieve the single target tracking of vehicles or pedestrians in 3D point cloud, as well as the multi-target tracking of pedestrians. Wang et al.  applied the Doppler effect to the real-time discrimination system of road traffic conflicts, and used Vissim simulation of the Doppler radar detection system to display the interface traffic operation status, and verified the effectiveness of the technology. Traffic operation situation prediction and traffic conflicts discrimination provide real-time reliable information support. Asvadi et al.  proposed a three-dimensional sensing system based on stereo and plane for 3D laser radar installed on intelligent vehicles. The sensing system is directly applied to the safety system of intelligent vehicles, especially in the detection of pedestrians and bicycles. Based on 3D lidar for pedestrian recognition and tracking of unmanned vehicles, Wang et al. BIB006 used the Support Vector Machine (SVM) to identify pedestrians, anticipate real-time pedestrian movement, and predict collision risk. Singh et al. BIB007 developed the fuzzy lidar control model using the brake and throttle opening as parameters. The throttle opening value decreases as the obstacle gets closer to the vehicle, and the speed is adjusted to ensure smooth braking and deceleration. Akai et al. BIB008 proposed a vehicle trajectory estimation and localization method based on multi-layer lidar combined with extended Kalman filter. Wu et al. BIB009 used 3D lidar point cloud as output, and used convolutional neural network and conditional random field to segment road objects in real time, distinguishing road objects and predicting their categories. Zhao et al. BIB010 used lidar sensors to accurately detect and track pedestrians and vehicles at intersections. According to the collected lidar data, they were analyzed in the processes of background filtering, target clustering, pedestrian vehicle classification, tracking pedestrians and vehicles, And obtaining accurate real-time information of presence, location, speed and direction. With the advantages of all-weather operation, high detection sensitivity and strong anti-interference ability, lidar becomes one of the mainstream environment sensing sensors of intelligent vehicle. At the same time, it has the disadvantages of large size, easy to be affected by weather conditions such as rain and snow, and high target loss rate. Its high cost has prevent it from entering the large-scale commercial phase.