Learning while Teaching for Autonomous Driving

Autonomous driving decision-making faces significant challenges due to complex environments and dynamic uncertainties. While knowledge distillation offers potential, conventional fixed teacher-student roles and unidirectional knowledge transfer limit flexibility and efficiency. To this end, we introduce the Learning while Teaching (LwT) framework. LwT establishes a dual-model reinforcement learning structure with dynamic role switching, enabling bidirectional knowledge transmission. LwT is a dual-flexible framework, allowing the primary model to learn through three synergistic pathways: direct environment interaction, auxiliary feedback, and auxiliary guidance. Addressing key challenges of primary model expressiveness and malignant positive feedback, LwT incorporates: (i) a Tri-Group Synergistic Reinforcement Learning for enhanced expressiveness via joint multi-group optimization, and (ii) a Collaborative Feedback Arbiter utilizing a self-distilled Kolmogorov-Arnold Network to ensure beneficial teaching-learning interactions. Extensive experiments demonstrate that LwT significantly outperforms baselines, achieving 15.68% higher performance in simulation and a 13.3% increase in real-world success rate, while effectively improving training efficiency and decision-making quality in autonomous driving systems.