Core Viewpoint - The article discusses the advancement of end-to-end autonomous driving systems, highlighting the introduction of the DIVER framework, which combines diffusion models and reinforcement learning to enhance trajectory diversity and safety in complex driving scenarios [3][33]. Group 1: Current Challenges in Autonomous Driving - Current end-to-end autonomous driving methods primarily rely on imitation learning from a single expert demonstration, leading to a lack of behavioral diversity and overly conservative planning in complex traffic situations [5][6]. - The existing models tend to converge around a single ground truth trajectory, resulting in limited exploration of diverse and safe decision-making options [7][8]. Group 2: Introduction of DIVER Framework - The DIVER framework integrates the multimodal generation capabilities of diffusion models with the goal-oriented constraints of reinforcement learning, transforming trajectory generation into a strategy generation problem under safety and diversity constraints [9][33]. - DIVER aims to produce multiple feasible and semantically valid candidate trajectories, addressing the limitations of traditional imitation learning approaches [9][33]. Group 3: Technical Innovations of DIVER - DIVER employs a Policy-Aware Diffusion Generator (PADG) that incorporates contextual information such as maps and dynamic agents, ensuring that generated trajectories are both semantically clear and feasible [16][20]. - The framework utilizes multiple reference ground truths to align each predicted trajectory with a specific driving intention, thereby preventing mode collapse and enhancing diversity [20][21]. Group 4: Performance Metrics and Results - In various benchmark evaluations, DIVER significantly outperformed existing methods in terms of trajectory diversity and safety, achieving lower collision rates while expanding the range of behaviors covered [28][30]. - The DIVER framework demonstrated superior performance in long-term planning tasks, maintaining the lowest collision rates while achieving higher diversity metrics compared to competitors [32][36]. Group 5: Conclusion and Implications - DIVER represents a significant step towards more human-like decision-making in autonomous driving by addressing the long-standing issues associated with imitation learning [33][34]. - The integration of generative models with reinforcement learning is positioned as a crucial advancement for the future of realistic autonomous driving applications [34].