Core Insights - The article discusses the introduction of ReAL-AD, a reasoning-enhanced learning framework for end-to-end autonomous driving, which aims to align decision-making processes with human cognitive models [2][8][40]. Group 1: Framework Overview - ReAL-AD integrates a three-layer human cognitive model (driving strategy, driving decision, and driving operation) into the decision-making process of autonomous driving [2][8]. - The framework includes three main components: 1. Strategic Reasoning Injector, which formulates high-level driving strategies from complex traffic insights generated by visual-language models (VLMs) [8][20]. 2. Tactical Reasoning Integrator, which refines driving intentions into interpretable driving choices [8][20]. 3. Hierarchical Trajectory Decoder, which translates driving decisions into precise control actions for smooth and human-like trajectory execution [8][20]. Group 2: Performance Evaluation - Extensive evaluations on the NuScenes and Bench2Drive datasets demonstrate that ReAL-AD improves planning accuracy and safety by over 30% compared to baseline methods [9][34]. - The method reduces L2 error by 33% and collision rates by 32%, indicating significant enhancements in trajectory accuracy and driving safety [9][34]. Group 3: Comparison with Existing Methods - Existing end-to-end autonomous driving methods often rely on fixed and sparse trajectory supervision, which limits their ability to replicate the structured cognitive reasoning processes of human drivers [3][10]. - ReAL-AD addresses these limitations by embedding structured multi-stage reasoning into the decision-making hierarchy, enhancing generalization capabilities in diverse real-world scenarios [5][10]. Group 4: Experimental Results - The framework outperforms other state-of-the-art methods, achieving the lowest average L2 error of 0.48 meters and a collision rate of 0.15% on the NuScenes dataset [34]. - In closed-loop evaluations, the integration of ReAL-AD significantly improves driving scores and success rates, demonstrating its effectiveness in real-world applications [34].