Core Viewpoint - The article discusses the limitations of existing Chain-of-Thought (CoT) reasoning methods in Vision-Language-Action (VLA) models for autonomous driving, particularly in simple scenarios where they do not improve decision quality and introduce unnecessary computational overhead. It introduces AdaThinkDrive, a new VLA framework that employs a dual-mode reasoning mechanism inspired by the "fast and slow thinking" theory, allowing the model to adaptively choose when to reason based on scene complexity [3][4][10]. Group 1: Introduction and Background - The shift from traditional modular approaches to end-to-end architectures in autonomous driving systems is highlighted, noting that while modular methods offer flexibility, they suffer from information loss between components, leading to cumulative errors in complex scenarios. End-to-end methods mitigate this issue but are still limited by their reliance on supervised data [7]. - The article categorizes current VLA methods into two paradigms: meta-action methods focusing on high-level guidance and planning-based methods that predict trajectories directly from raw inputs. The application of CoT techniques is becoming more prevalent, particularly in complex scenarios, but their effectiveness in simple scenarios is questioned [14][15]. Group 2: AdaThinkDrive Framework - AdaThinkDrive is proposed as an end-to-end VLA framework that incorporates a "fast answer/slow thinking" mechanism, allowing the model to switch adaptively between direct prediction and explicit reasoning based on scene complexity. This is achieved through a three-stage adaptive reasoning strategy [11][18]. - The framework's performance is validated through extensive experiments on the Navsim benchmark, achieving a Predictive Driver Model Score (PDMS) of 90.3, which is 1.7 points higher than the best pure visual baseline model. The model demonstrates superior adaptive reasoning capabilities, selectively enabling CoT in 96% of complex scenarios and defaulting to direct trajectory prediction in 84% of simple scenarios [4][18][50]. Group 3: Experimental Results and Analysis - The article presents a comprehensive evaluation of AdaThinkDrive against existing models, showing that it outperforms both "always think" and "never think" baseline models, with PDMS improvements of 2.0 and 1.4 points, respectively. Additionally, the reasoning time is reduced by 14% compared to the "always think" baseline, indicating a balance between accuracy and efficiency [4][18][58]. - The results indicate that the optimal reasoning strategy is not universal but depends on scene complexity, emphasizing the need for models to adaptively enable reasoning based on the context [10][18]. Group 4: Conclusion - The article concludes that reasoning in simple scenarios often increases computational costs without enhancing decision quality. AdaThinkDrive addresses this by allowing agents to learn when to think, guided by an adaptive thinking reward mechanism. The experimental results on the NAVSIM benchmark demonstrate that AdaThinkDrive achieves state-of-the-art performance, underscoring the importance of adaptive thinking for accurate and efficient decision-making in autonomous driving systems [66].