Intention-aware Human–Machine shared control for integrated Lateral–Longitudinal path tracking with liquid neural network

Human–machine shared control is a critical transitional paradigm toward safe and trustworthy vehicle automation, which fundamentally depends on accurate intention understanding and adaptive authority allocation. To this end, this paper proposes an intention-aware human–machine shared control framework for integrated lateral–longitudinal path tracking. Firstly, a liquid neural network (LNN)-based driver intention predictor is developed to jointly capture dynamically coupled steering and acceleration intentions, where continuous-time, input-modulated neuron dynamics enable adaptive representation of nonlinear and time-varying driving behavior. Then, an intention-aware reinforcement learning-based authority allocation strategy is designed to explicitly regulate lateral and longitudinal control authority in a closed-loop manner. Finally, the proposed framework is validated through driver-in-the-loop experiments under representative driving scenarios. The experimental results show that the proposed shared control strategy significantly improves the human–machine cooperation quality, and achieves a balanced performance among tracking accuracy, driving efficiency, ride comfort, and personalization across drivers with different skills.