Hybrid Trajectory Planning for Connected and Autonomous Vehicle Considering Communication Spoofing Attacks

In this study, we introduce a novel hybrid trajectory planning algorithm for autonomous driving, specifically designed to mitigate the risks posed by spoofing attacks on Connected and Autonomous Vehicles (CAVs). The research begins by assessing the safety implications of attacks and developing an adaptive safety model that is grounded in the fundamental assessment of state and decision data. This model incorporates the establishment of a posterior probability distribution for decision data, rooted in the pre-existing prior distribution but adjusted to account for the influence of spoofing attacks. The adjustment is achieved through Bayesian maximum posterior estimation, thereby refining the model to better adapt to potential threats. The adaptive safety model is then optimized dynamically, taking into consideration a set of indices—safety, comfort, and efficiency—that are critical to trajectory planning. In the subsequent phase, we introduce a composite trajectory planning algorithm that integrates a lateral trajectory selection sampling method with a longitudinal trajectory optimization approach. The adaptive safety model is seamlessly integrated into the trajectory planning process, influencing target position selection, the setting of constraints, and the formulation of the optimization objective function. The results demonstrate that the algorithm effectively limits the average standard deviation of lateral displacement to 0.5851 and achieves a significant increase in longitudinal speed growth rate, by up to 7.30%, surpassing the performance of benchmark algorithms. The proposed solution consistently delivers optimal safety and efficiency across various scenarios and under different parameter conditions.