Safe Headway Design for Autonomous Vehicles

Research Team: Junshan Zhang (lead), Mollie D’Agostino, Jiaqi Ma, Wei Shao, Chia-Ju Chen, Zhaofeng Zhang, and Zejun Fan

UC Campus(es): UC Davis

Problem Statement: In the coming decades, advancements in connected and automated vehicles (CAVs) have the potential to transform roadway safety. Headway, namely the distance between vehicles, is a key design factor for ensuring the safe operation of autonomous driving systems. There have been studies on headway optimization based on the speeds of leading and trailing vehicles, assuming perfect sensing capabilities. In practical scenarios, however, sensing errors are inevitable, calling for a more robust headway design to mitigate the risk of collision. Undoubtedly, augmenting the safety distance would reduce traffic throughput, highlighting the need for headway design to incorporate both sensing errors and risk tolerance models. In addition, prioritizing group safety over individual safety is often deemed unacceptable because no driver should sacrifice their safety for the safety of others.

Project Description: This project proposes a multi-objective optimization framework that examines the impact of sensing errors on both traffic throughput and the fairness of safety among vehicles. The proposed framework provides a solution to determine the Pareto frontier for traffic throughput and vehicle safety. ComDrive, a communication-based autonomous driving simulation platform, was developed to validate the proposed approach. Extensive experiments demonstrate that the proposed approach outperforms existing baselines.

Status: In Progress

Budget: $30,000