The reliability of environmental perception is the key to ensuring the stability and safety of autonomous vehicles. Complex road environments pose attentional challenges to automated driving, especially for vehicle perception systems. Therefore, evaluating the performance of autonomous driving algorithms in complex road environments is of great significance. However, as road geometry is complex, with many factors, including curvature, slope, lane marking conditions, and so on, the existing approach in assessing an algorithm through detection tests is of low efficiency. Therefore, this study relies on failure prediction modeled by extreme gradient boosting and recursive feature elimination. An evaluation model that could accomplish a quick set of road evaluations is developed. The proposed model generates recognition reliability merely by such features as road design parameters. In addition, road curvature is the most significant environmental factor affecting the perception of autonomous vehicles. The failure rates of the algorithm in different scenarios demonstrate a negative correlation between the radius of curvature and failure perception. The partial dependence involving the radius of curvature and the perception ability is obtained. Furthermore, other features, such as missing lane lines and illumination conditions, are taken into consideration to explore the potential failure perception mode. The proposed method has a particular significance for finding the failure mode of an autonomous driving perception algorithm.

With the aim of promoting energy conservation and emission reduction, electric vehicles (EVs) have gained significant attention as a strategic industry in many countries. However, the insufficiency of accessible charging infrastructure remains a challenge, hindering the widespread adoption of EVs. To address this issue, we propose a novel approach to optimize the placement of charging stations within a road network, known as the charging station location problem (CSLP). Our method considers multiple factors, including fairness in charging station distribution, benefits associated with their placement, and drivers’ discomfort. Fairness is quantified by the balance in charging station coverage across the network, while driver comfort is measured by the total time spent during the charging process. Then, the CSLP is formulated as a reinforcement learning problem, and we introduce a novel model called PPO-Attention. This model incorporates an attention layer into the Proximal Policy Optimization (PPO) algorithm, enhancing the algorithm’s capacity to identify and understand the intricate interdependencies between different nodes in the network. We have conducted extensive tests on urban road networks in Europe, North America, and Asia. The results demonstrate the superior performance of our approach compared to existing baseline algorithms. On average, our method achieves a profit increase of 258.04% and reduces waiting time by 73.40%, travel time by 18.46%, and charging time by 40.10%.