Core Viewpoint - The article discusses the development of SafeMVDrive, a framework designed to generate high-fidelity, multi-view safety-critical driving videos for testing autonomous driving systems in extreme scenarios, addressing the challenges of real-world data collection and simulation limitations [7][11][30]. Group 1: Safety Testing Challenges - Current autonomous driving systems struggle to avoid accidents in high-risk scenarios such as night construction sites and sudden obstacles, indicating a need for improved reliability in these situations [2][3]. - Extreme scenarios are infrequent in real-world conditions, making data collection difficult, while existing simulators lack the realism required for effective testing [5][6]. Group 2: SafeMVDrive Framework - SafeMVDrive combines a Visual Language Model (VLM) for vehicle selection with a two-stage trajectory generation process to create high-fidelity safety-critical videos for testing [7][10]. - The framework addresses two main challenges: accurately selecting safety-critical vehicles and ensuring the generalization of multi-view video generation models [9][10]. Group 3: Innovations in Vehicle Selection and Trajectory Generation - The VLM-based vehicle selector utilizes visual information to identify potentially dangerous vehicles, improving upon traditional heuristic methods [19][31]. - The two-stage trajectory generation process first simulates collision trajectories and then transforms them into avoidance trajectories, maintaining the critical safety features while ensuring realistic video generation [20][22][23]. Group 4: Video Generation and Evaluation - SafeMVDrive employs a multi-view video generation module to convert avoidance trajectories into high-fidelity videos, ensuring both safety-criticality and visual realism [25][26]. - The framework significantly enhances the coverage and diversity of safety-critical scenarios compared to existing methods, demonstrating superior performance in generating challenging test data [28][30]. Group 5: Performance Metrics - SafeMVDrive shows improved metrics in sample-level and scene-level collision rates, indicating its effectiveness in generating realistic and challenging driving scenarios [29][30]. - The VLM vehicle selector achieves a balance of precision and recall, ensuring that the selected vehicles align with real traffic logic, which is crucial for effective simulation [32].