Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner

An Improved Object Detection and Trajectory Prediction Method for Traffic Conflicts Analysis

Although computer vision-based methods have seen broad utilisation in evaluating traffic situations, there is a lack of research on the assessment and prediction of near misses in traffic. In addition, most object detection algorithms are not very good at detecting small targets. This study proposes a combination of object detection and tracking algorithms, Inverse Perspective Mapping (IPM), and trajectory prediction mechanisms to assess near-miss events. First, an instance segmentation head was proposed to improve the accuracy of the object frame box detection phase. Secondly, IPM was applied to all detection results. The relationship between them is then explored based on their distance to determine whether there is a near-miss event. In this process, the moving speed of the target was considered as a parameter. Finally, the Kalman filter is used to predict the object\u27s trajectory to determine whether there will be a near-miss in the next few seconds. Experiments on Closed-Circuit Television (CCTV) datasets showed results of 0.94 mAP compared to other state-of-the-art methods. In addition to improved detection accuracy, the advantages of instance segmentation fused object detection for small target detection are validated. Therefore, the results will be used to analyse near misses more accurately

Visual Counting of Traffic Flow from a Car via Vehicle Detection and Motion Analysis

Visual traffic counting so far has been carried out by static cameras at streets or aerial pictures from sky. This work initiates a new approach to count traffic flow by using populated vehicle driving recorders. Mainly vehicles are counted by a camera moves along a route on opposite lane. Vehicle detection is first implemented in video frames by using deep learning YOLO3, and then vehicle trajectories are counted in the spatial-temporal space called motion profile. Motion continuity, direction, and detection missing are considered to avoid multiple counting of oncoming vehicles. This method has been tested on naturalistic driving videos lasting for hours. The counted vehicle numbers can be interpolated as a flow of opposite lanes from a patrol vehicle for traffic control. The mobile counting of traffic is more flexible than the traffic monitoring by cameras at street corners