A Human-Oriented Cooperative Driving Approach: Integrating Driving Intention, State, and Conflict

TL;DR

This paper introduces a human-oriented cooperative driving approach that aligns vehicle actions with driver intentions and states, improving safety, efficiency, and human-machine harmony in autonomous driving systems.

Contribution

It presents a novel HOCD framework integrating intention-aware trajectory planning and reinforcement learning-based authority allocation to reduce conflicts and enhance cooperation.

Findings

Improved alignment of vehicle trajectory with driver intention.

Enhanced cooperation and reduced conflict in human-vehicle interaction.

Better driving performance compared to existing approaches.

Abstract

Human-vehicle cooperative driving serves as a vital bridge to fully autonomous driving by improving driving flexibility and gradually building driver trust and acceptance of autonomous technology. To establish more natural and effective human-vehicle interaction, we propose a Human-Oriented Cooperative Driving (HOCD) approach that primarily minimizes human-machine conflict by prioritizing driver intention and state. In implementation, we take both tactical and operational levels into account to ensure seamless human-vehicle cooperation. At the tactical level, we design an intention-aware trajectory planning method, using intention consistency cost as the core metric to evaluate the trajectory and align it with driver intention. At the operational level, we develop a control authority allocation strategy based on reinforcement learning, optimizing the policy through a designed reward function to achieve consistency between driver state and authority allocation. The results of simulation and human-in-the-loop experiments demonstrate that our proposed approach not only aligns with driver intention in trajectory planning but also ensures a reasonable authority allocation. Compared to other cooperative driving approaches, the proposed HOCD approach significantly enhances driving performance and mitigates human-machine conflict.The code is available at https://github.com/i-Qin/HOCD.