Abstract:

With the advancement of smart city development and precise navigation technologies, pedestrian path planning research has gradually shifted from a single efficiency-oriented paradigm to one driven by multidimensional and personalized demands. The primary objective is to develop path planning models that account for complex urban environments and individual user preferences, thereby providing efficient, flexible, and personalized route recommendations for pedestrians. However, current research still faces key challenges, including insufficient modeling of group heterogeneity, the absence of dynamic preference mechanisms, and limited adaptability to complex scenarios. To address these issues, This paper proposes a pedestrian path planning method based on multi-dimensional preference modeling and adversarial deep reinforcement learning. The proposed method first constructs a “context-aware and dynamically adaptive” multidimensional preference model, which provides dynamic preference weights for pedestrian route selection. These weights guide the reshaping of the reward function in the deep reinforcement learning framework, enabling a multi-objective collaborative optimization mechanism that balances efficiency, safety, and comfort. Subsequently, a preference-enhanced adversarial deep Q-network algorithm (PEA-DQN) is developed, incorporating a dual-experience replay pretraining strategy and an adaptive training mechanism to accelerate model convergence and reduce redundant computation. Experiments conducted in Wuhan under dynamic disturbances within a mixed urban road network validate the performance of the model trained by PEA-DQN. Compared with the DQN algorithm, PEA-DQN improves obstacle-avoidance success rates by more than 50% and reduces average path length by 40.40%. Ablation studies further demonstrate that, relative to Dueling DQN, the incorporation of a multi-objective reward function improves path quality by 100.4%, while the adaptive mechanism increases computational efficiency by 40% in dynamic obstacle scenarios. Overall, PEA-DQN significantly outperforms dynamic A^* algorithm and other comparable deep reinforcement learning approaches.

Key words: pedestrian path planning, deep reinforcement learning, multi-dimensional preference modeling, personalized mobility