用GPU加速蒙特卡洛模拟，实时评估刹车系统的不确定性风险。
Real-Time GPU-Accelerated Monte Carlo Evaluation of Safety-Critical AEB Systems Under Uncertainty
- 基于高保真车辆模型的并行蒙特卡洛框架，每样本独立计算
- 最高提速54.57倍，且与CPU结果完全一致
- 可在嵌入式平台实现实时概率评估，适合车载安全系统
自动紧急制动（AEB）系统属于关乎公共安全的关键技术，美国国家公路交通安全管理局（NHTSA）要求自2029年9月起所有新售轻型车均需配备AEB。然而现有实现多依赖确定性停车距离或碰撞时间（TTC）阈值，无法捕捉感知、道路条件和车辆动力学中的不确定性。本文提出一种基于GPU加速的蒙特卡洛评估框架，采用包含气动阻力、道路坡度、制动执行器动态及质心转移效应的高保真纵向车辆模型。通过每样本一线程的执行策略，利用蒙特卡洛推演间的独立性；同时采用确定性CPU采样，确保CPU与GPU间数值完全一致。在四种硬件平台（两台笔记本显卡GTX 1650、RTX 5070，以及两款车载级嵌入式平台Jetson Orin Nano、Jetson AGX Orin）上验证，峰值速度提升达54.57倍，且数值精确一致。结合完整AEB时序预算（700毫秒人类反应时间减去120毫秒感知与50毫秒决策开销），分析表明Jetson AGX Orin可在530毫秒内完成约25,000次蒙特卡洛采样，支持将概率化评估作为嵌入式系统运行时组件。该成果确立了蒙特卡洛不确定性评估可作为部署式运行模块，而非仅限离线验证，并为应对NHTSA最终规则下风险感知的阈值选择提供量化依据。
原文摘要 · Abstract (English)
Automatic Emergency Braking (AEB) systems represent a safety-critical national interest, with the National Highway Traffic Safety Administration (NHTSA) Federal Motor Vehicle Safety Standard (FMVSS No. 127) requiring AEB in all new light vehicles sold in the United States by September 2029. However, production implementations frequently rely on deterministic stopping-distance or Time-to-Collision (TTC) thresholds that fail to capture uncertainty in sensing, road conditions, and vehicle dynamics. This paper presents a GPU-accelerated Monte Carlo framework for stochastic evaluation of emergency braking performance using a high-fidelity longitudinal vehicle model incorporating aerodynamic drag, road grade, brake actuator dynamics, and weight transfer effects. A one-thread-per-sample execution strategy exploits the independence of Monte Carlo rollouts, while deterministic CPU-generated sampling ensures bit-exact numerical consistency between CPU and GPU implementations. The framework is evaluated across four hardware platforms spanning development and deployment environments: two laptop GPUs (GTX 1650, RTX 5070) and two automotive-grade embedded platforms (Jetson Orin Nano, Jetson AGX Orin). Peak speedups of 54.57x are achieved while maintaining exact numerical agreement. Real-time feasibility analysis with a complete AEB timing budget (700 ms human reaction time minus 120 ms perception and 50 ms decision overhead) demonstrates that the Jetson AGX Orin can execute approximately 25,000 Monte Carlo samples within a 530 ms budget, enabling real-time probabilistic AEB evaluation as part of a complete embedded pipeline. These results establish Monte Carlo-based uncertainty evaluation as a deployable runtime component rather than an offline validation tool and provide quantitative guidance for risk-aware AEB threshold selection under the NHTSA final rule.
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