Cooperative Perception for Estimating and Predicting Microscopic Traffic States to Manage Connected and Automated Traffic

Real-time traffic state estimation and prediction are of importance to the traffic management systems. New opportunities are enabled by the emerging sensing and automation technologies to manage connected and automated traffic, particularly in terms of controlling trajectories of automated vehicles. Traffic information from connected and automated vehicles (CAV) and roadside detectors (RSD) can be fused and has great potential for providing detailed microscopic traffic states (i.e., vehicle speeds, positions) of all vehicles. In this paper, we propose a cooperative perception framework for this purpose. The proposed framework based on particle filtering is developed to provide an accurate estimation and prediction of the microscopic states of partially observed traffic systems, while accounting for different sources of errors that intrinsically exist in the system, including those from sensor data, vehicle movement, and process models. Selected freeway and arterial vehicle trajectory datasets from the Next Generation Simulation (NGSIM) program and CAV traffic simulation are applied to test the proposed methodological framework. The accuracy of position and speed estimation is between 50% and 70% when the CAV market penetration rate (MPR) is 12.5%, and between 80% and 90% when the MPR is 50%. The incorporation of RSD data can further increase the accuracy by up to 10% under low CAV MPRs. The framework can also provide an accurate short-term prediction (i.e., 5 - 15 seconds) of position and speed with 60% to 90% accuracy. The proposed framework provides efficient and accurate estimations and predictions of detailed microscopic traffic states, even at low CAV MPRs, creating dynamic traffic environment world models to enable fine control and management of the connected and automated traffic systems.

Automated summary

The proposed cooperative perception framework based on particle filtering provides efficient and accurate estimations and predictions of detailed microscopic traffic states by combining data from connected and automated vehicles and roadside detectors. This framework demonstrates significant improvements in accuracy and predictive performance for real-time traffic state estimation and prediction.