Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions

TL;DR

This paper develops and evaluates autonomous vehicle behaviors in fully automated and mixed traffic conditions, highlighting the importance of V2V communication and human interaction challenges for safety and cooperation.

Contribution

It introduces a multi-stage approach to AV development, incorporating sharing and caring V2V strategies and assesses human reactions in mixed traffic scenarios.

Findings

Deep reinforcement learning enables AVs to mimic human driving behavior.

Sharing and caring V2V communication improves AV coordination and safety.

Human ego-driven actions pose challenges to safety in mixed traffic.

Abstract

Autonomous Vehicle (AV) technology is advancing rapidly, promising a significant shift in road transportation safety and potentially resolving various complex transportation issues. With the increasing deployment of AVs by various companies, questions emerge about how AVs interact with each other and with human drivers, especially when AVs are prevalent on the roads. Ensuring cooperative interaction between AVs and between AVs and human drivers is critical, though there are concerns about possible negative competitive behaviors. This paper presents a multi-stage approach, starting with the development of a single AV and progressing to connected AVs, incorporating sharing and caring V2V communication strategy to enhance mutual coordination. A survey is conducted to validate the driving performance of the AV and will be utilized for a mixed traffic case study, which focuses on how the human drivers will react to the AV driving alongside them on the same road. Results show that using deep reinforcement learning, the AV acquired driving behavior that reached human driving performance. The adoption of sharing and caring based V2V communication within AV networks enhances their driving behavior, aids in more effective action planning, and promotes collaborative behavior amongst the AVs. The survey shows that safety in mixed traffic cannot be guaranteed, as we cannot control human ego-driven actions if they decide to compete with AV. Consequently, this paper advocates for enhanced research into the safe incorporation of AVs on public roads.