Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy

Transfer learning improves policy learning efficiency by reusing knowledge from source tasks, providing a feasible paradigm for safe and efficient autonomous highway lane changing decision-making. Existing methods frequently encounter transfer mismatch induced by distribution shifts between source and target domains, leading to training oscillation and performance decline. Besides, target domain adaptation depends on exploratory interactions, which struggles to guarantee training safety in safety-critical lane changing cases. To tackle these limitations, this paper proposes a safe transfer reinforcement learning framework for autonomous highway lane changing. First, we design an adaptive teacher intervention mechanism based on instantaneous safety cost to restrain risky exploration and fade intervention strength progressively, with theoretical analysis on return bounds for mixed behavior policy. This intervention also produces dual-source samples for joint training. Second, a teacher-guided safe transfer module embeds action evaluation information of teacher policy into student learning via reward shaping to boost training safety and efficiency, with teacher guidance decaying as policy safety rises. Third, a teacher-guided weighted optimization mechanism adjusts sample weights in policy optimization using a likelihood ratio factor to stabilize transfer performance. Experiments under varied traffic densities and validations on real-world NGSIM dataset reveal that our method surpasses baseline approaches by over 52.2% in safety and 5.0% in learning efficiency. Results verify the efficacy and robustness of our safety-aware transfer strategy for autonomous highway lane changing under various traffic conditions.

Multimodal Classification Network Guided Trajectory Planning for Four-Wheel Independent Steering Autonomous Parking Considering Obstacle Attributes

Four-wheel Independent Steering (4WIS) vehicles have attracted increasing attention for their superior maneuverability. Human drivers typically choose to cross or drive over the low-profile obstacles (e.g., plastic bags) to efficiently navigate through narrow spaces, while existing planners neglect obstacle attributes, causing inefficiency or path-finding failures. To address this, we propose a trajectory planning framework integrating the 4WIS hybrid A* and Optimal Control Problem (OCP), in which the hybrid A* provides an initial path to enhance the OCP solution. Specifically, a multimodal classification network is introduced to assess scene complexity (hard/easy task) by fusing image and vehicle state data. For hard tasks, guided points are set to decompose complex tasks into local subtasks, improving the search efficiency of 4WIS hybrid A*. The multiple steering modes of 4WIS vehicles (Ackermann, diagonal, and zero-turn) are also incorporated into node expansion and heuristic designs. Moreover, a hierarchical obstacle handling strategy is designed to guide the node expansion considering obstacle attributes, i.e., 'non-traversable', 'crossable', and 'drive-over' obstacles. It allows crossing or driving over obstacles instead of the 'avoid-only' strategy, greatly enhancing success rates of pathfinding. We also design a logical constraint for the 'drive-over' obstacle by limiting its velocity to ensure safety. Furthermore, to address dynamic obstacles with motion uncertainty, we introduce a probabilistic risk field model, constructing risk-aware driving corridors that serve as linear collision constraints in OCP. Experimental results demonstrate the proposed framework's effectiveness in generating safe, efficient, and smooth trajectories for 4WIS vehicles, especially in constrained environments.