A reliability analysis method based on cross-entropy importance sampling for lateral safety assessment of misaligned autonomous truck platoons under crosswinds
提出一种基于交叉熵重要性抽样的方法，高效评估错位自动驾驶卡车队列在侧风下的横向风险概率，相比蒙特卡洛模拟大幅减少计算时间，为智能货运系统的实时安全管理和动态协调提供支持。
Autonomous truck platooning offers a range of advantages in freight transportation, including energy efficiency and operational effectiveness, and has garnered increasing attention. But traditional truck platoons, typically aligned in a straight line, can cause channelized damage to road surfaces due to the concentrated load over short durations. To enhance pavement sustainability, the concept of misaligned truck platoons has been explored, wherein trucks within the platoon intentionally drift laterally by varying amounts. Nevertheless, the misalignment introduces concerns regarding the lateral safety performance of the trucks. To investigate the lateral safety of misaligned autonomous truck platoons under crosswinds, this paper introduces an innovative and efficient approach. Using a three-truck platoon as an example, Cross-Entropy Importance Sampling (CE-IS) is utilized to estimate the lateral risk probability of misaligned truck platoons with a randomized distribution across multiple indicators, and the results are compared with those obtained using the Monte Carlo Simulation (MCS). The results indicate that CE-IS significantly reduces computation time while maintaining high accuracy. This computational efficiency makes CE-IS particularly suitable for intelligent freight systems, where rapid and reliable risk assessment is critical for dynamic platoon coordination, real-time safety management, and optimization under uncertain conditions. Under a consistent in-lane lateral distribution control mode, misaligned truck platoons are more prone than aligned platoons to encroaching into adjacent lanes, thereby increasing the risk of accidents. However, reorganizing the middle truck in a misaligned platoon to establish a misalignment distance away from the lane marking can reduce the lateral risk probability, though it remains slightly higher than that of aligned platoons. This study contributes to the efficient and accurate quantification of lateral risk in vehicle platoon systems. Additionally, it provides valuable insights into enhancing the lateral safety of coordinated truck platoons and mitigating their impact on road surfaces. And this method combining CE-IS proposed in this study can efficiently quantify and calculate the lateral risk of vehicle platoons in intelligent freight systems, providing suggestions for real-time operational strategies.