Core Viewpoint - The article discusses the ReAL-AD framework, which integrates human-like reasoning into end-to-end autonomous driving systems, enhancing decision-making processes through a structured approach that mimics human cognitive functions [3][43]. Group 1: Framework Overview - ReAL-AD employs a reasoning-enhanced learning framework based on a three-layer human cognitive model: driving strategy, decision-making, and operation [3][5]. - The framework incorporates a visual-language model (VLM) to improve environmental perception and structured reasoning capabilities, allowing for a more nuanced decision-making process [3][5]. Group 2: Components of ReAL-AD - The framework consists of three main components: 1. **Strategic Reasoning Injector**: Utilizes VLM to generate insights for complex traffic situations, forming high-level driving strategies [5][11]. 2. **Tactical Reasoning Integrator**: Converts strategic intentions into executable tactical choices, bridging the gap between strategy and operational decisions [5][14]. 3. **Hierarchical Trajectory Decoder**: Simulates human decision-making by establishing rough motion patterns before refining them into detailed trajectories [5][20]. Group 3: Performance Evaluation - In open-loop evaluations, ReAL-AD demonstrated significant improvements over baseline methods, achieving over 30% better performance in L2 error and collision rates [36]. - The framework achieved the lowest average L2 error of 0.48 meters and a collision rate of 0.15% on the nuScenes dataset, indicating enhanced learning efficiency in driving capabilities [36]. - Closed-loop evaluations showed that the introduction of the ReAL-AD framework significantly improved driving scores and successful path completions compared to baseline models [37]. Group 4: Experimental Setup - The evaluation utilized the nuScenes dataset, which includes 1,000 scenes sampled at 2Hz, and the Bench2Drive dataset, covering 44 scenarios and 23 weather conditions [34]. - Metrics for evaluation included L2 error, collision rates, driving scores, and success rates, providing a comprehensive assessment of the framework's performance [35][39]. Group 5: Ablation Studies - Ablation studies indicated that removing the Strategic Reasoning Injector led to a 12% increase in average L2 error and a 19% increase in collision rates, highlighting its importance in guiding decision-making [40]. - The Tactical Reasoning Integrator was shown to reduce average L2 error by 0.14 meters and collision rates by 0.05%, emphasizing the value of tactical commands in planning [41]. - Replacing the Hierarchical Trajectory Decoder with a multi-layer perceptron resulted in increased L2 error and collision rates, underscoring the necessity of a hierarchical decoding process for trajectory prediction [41].