用贝叶斯优化加速自动驾驶场景验证，大幅减少仿真次数。
Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function
- 基于贝叶斯优化自动搜索高风险驾驶场景。
- 仅需传统方法千分之一的仿真量即可发现致命事故。
- 适用于复杂多维参数空间，适合自动驾驶安全验证团队。
自动驾驶功能（ADFs）的严格验证与确认（V&V）对保障自动驾驶汽车（AV）安全和公众接受度至关重要。当前验证依赖仿真以在车辆运行设计域（ODD）内实现充分测试覆盖，但全面探索潜在场景的庞大参数空间计算成本高且耗时。本文提出一种基于贝叶斯优化（BO）的框架，用于加速关键场景的发现。我们在基于模型预测控制（MPC）的运动规划器上验证了该框架的有效性，结果表明其能以远少于暴力实验设计（DoE）的方法，识别出如偏离道路等危险情形。此外，本研究还探讨了该框架在高维参数空间中的可扩展性，并成功识别出运动规划器在案例研究中多个独立的临界区域。
原文摘要 · Abstract (English)
Rigorous Verification and Validation (V&V) of Autonomous Driving Functions (ADFs) is paramount for ensuring the safety and public acceptance of Autonomous Vehicles (AVs). Current validation relies heavily on simulation to achieve sufficient test coverage within the Operational Design Domain (ODD) of a vehicle, but exhaustively exploring the vast parameter space of possible scenarios is computationally expensive and time-consuming. This work introduces a framework based on Bayesian Optimization (BO) to accelerate the discovery of critical scenarios. We demonstrate the effectiveness of the framework on an Model Predictive Controller (MPC)-based motion planner, showing that it identifies hazardous situations, such as off-road events, using orders of magnitude fewer simulations than brute-force Design of Experiments (DoE) methods. Furthermore, this study investigates the scalability of the framework in higher-dimensional parameter spaces and its ability to identify multiple, distinct critical regions within the ODD of the motion planner used as the case study .
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