AI 中文总结
针对自动驾驶安全验证，提出风险场增强闭环数字孪生框架，集成多方面功能，引入驾驶风险场描述风险，通过评估协议比较不同方法，该框架能让验证更具针对性、可解释性和可重用性，但受模型保真度等因素限制。
AI 中文摘要
自动驾驶系统在实际部署前需要可靠的安全验证。大规模道路测试成本高、难重现且难以暴露罕见的安全关键场景。传统模拟虽提高了可重复性，但单一仿射模拟器无法连续连接物理交通状态、虚拟重建、算法评估和场景演变。本文提出一种用于自动驾驶安全验证的风险场增强闭环数字孪生框架，集成了物理数据采集、数据同步、虚拟孪生重建、风险感知场景生成、自动驾驶算法评估和安全分析。引入驾驶风险场作为统一的中间表示来描述车辆周围的多种风险，对数字孪生场景库中的高风险场景进行排序，并为基于强化学习的驾驶策略提供密集安全指导。设计了模拟风格的评估协议来比较传统强化学习基线、风险惩罚基线和所提出的风险场引导方法。研究表明，将明确的风险结构嵌入数字孪生可使自动驾驶验证更具针对性、可解释性和可重用性，但其实际有效性仍受模型保真度、风险校准和模拟到现实转移的限制。
英文摘要
Autonomous driving systems require reliable safety validation before real-world deployment. However, large-scale road testing is costly, difffcult to reproduce, and inefffcient for exposing rare safety-critical scenarios. Conventional simulation improves repeatability, but an offfine simulator alone cannot continuously connect physical trafffc states, virtual reconstruction, algorithm evaluation, and scenario evolution. This paper proposes a risk-ffeld enhanced closed-loop digital twin framework for autonomous driving safety validation. The framework integrates physical data acquisition, data synchronization, virtual twin reconstruction, risk-aware scenario generation, autonomous driving algorithm evaluation, and safety analysis. A driving risk ffeld is introduced as a uniffed intermediate representation to describe obstacle, lane-departure, road-boundary, time-to-collision, and comfort-related risks around the ego vehicle. The risk ffeld ranks high-risk scenarios in the digital twin scenario library and provides dense safety guidance for reinforcement learning-based driving policies. A simulation-style evaluation protocol is designed to compare conventional reinforcement learning baselines, risk-penalty baselines, and the proposed risk-ffeld guided method. The study indicates that embedding explicit risk structure into digital twins can make autonomous driving validation more targeted, interpretable, and reusable, while its practical effectiveness remains bounded by model ffdelity, risk calibration, and sim-to-real transfer.
