Core Viewpoint - The article emphasizes the critical importance of simulation testing in the development of autonomous driving technologies, highlighting the need for high-confidence simulation platforms to ensure the reliability of algorithms and safety in real-world scenarios [2][3]. Group 1: Challenges in Simulation Technology Confidence - The three core challenges in achieving simulation confidence are sensor model bias, static scene distortion, and dynamic scene restoration errors [3][21]. - Sensor model bias arises from the simplification of complex physical processes, affecting the validity of simulation data [4][10]. - Static scene model bias impacts the reliability of perception and localization due to geometric, material, and lighting distortions [16][20]. Group 2: Sensor Model Bias - Camera model bias is primarily due to inaccuracies in modeling spectral, optical systems, and image signal processing (ISP) [5][8]. - LiDAR model bias stems from laser attenuation, multipath reflection, and return intensity modeling, which can distort point cloud data [10][11]. - Radar simulation faces challenges in both modeling and verification, particularly in accurately simulating radar cross-section (RCS) and multipath effects [12][15]. Group 3: Static Scene Model Bias - Geometric errors, such as millimeter-level deviations in road curvature and slope, can lead to significant issues in localization algorithms [17]. - Material errors arise from discrepancies between physical rendering parameters and real-world properties, while lighting errors can distort shadows and highlights, affecting visual feature-dependent algorithms [20][24]. Group 4: Dynamic Scene Restoration Bias - Dynamic scene challenges involve accurately reproducing spatiotemporal interactions, with errors arising from vehicle dynamics modeling and traffic flow reconstruction [21][22]. - Traffic flow and interaction behavior distortions can lead to significant discrepancies in the timing and nature of interactions between vehicles [23][24]. Group 5: High-Confidence Simulation Testing Pathways - To address the identified challenges, a layered and closed-loop verification system is proposed, ensuring fidelity from sensors to static and dynamic scenes [27][28]. - High-fidelity sensor modeling aims to minimize the gap between simulation data and real sensor outputs by adhering to physical rendering equations [29][30]. - Standardized verification processes are essential for ensuring consistency across different simulation platforms, including geometric, color, and photometric consistency assessments [31][33][48]. Group 6: Continuous Iterative Verification System - Building a high-confidence simulation for autonomous driving is a continuous, systematic engineering process that requires a deep understanding of error sources and the design of quantifiable validation metrics [62][63]. - The proposed framework aims to break down the abstract concept of "confidence" into specific, actionable engineering tasks, facilitating the gradual reduction of discrepancies between simulation and reality [63].