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

TL;DR

The paper addresses the challenge of transitioning toward fully autonomous driving by reducing human–machine conflict through a human-oriented cooperative driving (HOCD) framework. It combines intention-aware trajectory planning in the tactical domain with a reinforcement learning–based authority allocation in the operational domain to dynamically balance driver and automation control. The approach uses a Frenet-frame trajectory planner with an intention-consistency cost and employs PPO for adaptive authority allocation based on driver state, vehicle state, and driver inputs, validated through CARLA simulations and human-in-the-loop experiments. Results show HOCD reduces conflicts and improves safety, stability, and user trust compared with fixed or driver-characteristic baselines, providing a practical pathway for incremental autonomy and better human–machine collaboration.

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.

Figures (11)

• Figure 1: Three levels of human-vehicle cooperative driving.
• Figure 2: Framework of the proposed human-oriented cooperative driving approach.
• Figure 3: Difference between the Frenet frame and the Cartesian frame.
• Figure 4: Scenarios and routes in the CARLA driving simulator.
• Figure 5: Performance comparison curves for SAC, PPO, and DDPG.