This paper presents a methodology to detect rear-end conflicts at signalized intersections with the help of roadside LiDAR sensors. Raw data collected in the point cloud format from the sensors was processed using a series of data processing algorithms to obtain vehicle trajectories. Time-based (MTTC), deceleration-based (SDI), and severity-based (CSI) surrogate safety indices were calculated from the vehicle trajectories to identify the conflict threats at every frame of the dataset, which were further aggregated together to evaluate the risk exposure and risk severity at different temporal segments of the leader/follower car-following period to obtain a rear-end conflict index. The identified conflicts were compared with the historical crash records using negative binomial models. The results indicate correlation between the identified conflicts and the crashes, and further provide new information about the rear-end crash risks at the intersection which could support the proactive approach of traffic safety analysis.

In transportation, LiDAR has been primarily used in autonomous vehicles to assist self-driving until recently when people realized it could also be installed at the roadside to support connected vehicles and infrastructure systems. Unlike onboard LiDAR sensors used in autonomous vehicles, roadside applications must perform complete background filtering and clustering as well as tracking real-time traffic movements within the detection zone. This paper presents an unsupervised clustering method for roadside or infrastructure-based LiDAR applications. It first converts 3D LiDAR data points into 2D so that only target points (after background filtering) will be saved in the channel-azimuth 2D structure; then, a method combining the region growing algorithm and counted component labeling is used to perform clustering. Lastly, a merging process is conducted to enhance the connected component labeling method for better outcomes. Experimental studies demonstrate that the proposed method could reach 0.011s per frame (10 Hz sensor rotation frequency) in clustering while maintaining high accuracy.

Light detection and ranging (LiDAR) sensors are receiving an increasing amount of attention in traffic detection because of their powerful capacity for providing accurate trajectory data of vehicles and non-motorized road users. When installed at the roadside, LiDAR faces the same occlusion problem as other over-roadway sensors (such as video cameras)—the integrity and reliability of object detection can be reduced when occlusion occurs. Existing occlusion reasoning methods are either developed for video sensors or require sensor fusion. Fast and accurate occlusion determination is an indispensable step in the process of vehicle detection, classification, and tracking. This paper presents a novel algorithm to automatically identify the occurrence of vehicle partial occlusion and the corresponding occluding/occluded relationship from roadside LiDAR data. According to the inherent characteristics of LiDAR sensors, two specifically designed heatmaps (ClusterID heatmap and Distance heatmap) were generated and used as the basis for fast occlusion search. Using the field data collected from two testbeds, it was shown that the proposed method could achieve about 95.60% accuracy for partial occlusion identification and a processing speed of 2 ms per frame (32-laser LiDAR sensor with 0.1 s per frame).

Object occlusion is a common challenge in roadside LiDAR-based vehicle tracking. This issue can cause variances in vehicle location and speed calculations. This paper proposes a vehicle tracking post-processing method designed to handle full occlusion and limited datapoint conditions. The first part of the method focuses on linking the disconnected trajectories of the same vehicle caused by full occlusion. The second part refines the vehicle representative point to enhance tracking accuracy. Performance evaluation demonstrates that the proposed method can detect and reconnect the trajectories of the same vehicle, even under prolonged full occlusion. Moreover, the refined vehicle representative point provides more stable speed estimates, even with sparse datapoints. This significantly increases the effective detection range of roadside LiDAR. This approach lays a strong foundation for the application of roadside LiDAR in emission analysis and near-crash studies.