在硬件故障下识别风险显著的汽车场景
Identification of Risk Significant Automotive Scenarios Under Hardware Failures
AI总结：
本文提出基于马尔可夫/单元到单元映射技术的通用回溯过程算法，用于识别导致安全目标违反的关键场景，并通过混合状态控制系统案例研究验证其有效性。
摘要：
车辆控制系统中的自动驾驶功能水平持续上升，这使得控制系统工程师在确保高安全性的过程中面临更大挑战，尤其是在应对如随机硬件故障等意外故障时。本文提出了一种基于演绎实现的马尔可夫/单元到单元映射技术的通用回溯过程算法（BPA），用于识别导致安全目标违反的关键场景。系统离散状态空间表示允许追踪故障在整个系统中的传播，并量化时间内的概率系统演变。构建了一个易受制动线故障影响的自动驾驶车辆混合状态控制系统案例研究。所关注的危险是与静止车辆碰撞。BPA被用于识别导致该危险的关键风险场景。
英文摘要：
The level of autonomous functions in vehicular control systems has been on a steady rise. This rise makes it more challenging for control system engineers to ensure a high level of safety, especially against unexpected failures such as stochastic hardware failures. A generic Backtracking Process Algorithm (BPA) based on a deductive implementation of the Markov/Cell-to-Cell Mapping technique is proposed for the identification of critical scenarios leading to the violation of safety goals. A discretized state-space representation of the system allows tracing of fault propagation throughout the system, and the quantification of probabilistic system evolution in time. A case study of a Hybrid State Control System for an autonomous vehicle prone to a brake-by-wire failure is constructed. The hazard of interest is collision with a stationary vehicle. The BPA is implemented to identify the risk significant scenarios leading to the hazard of interest.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
