A novel autonomous driving motion planning framework for unsignalized intersections is presented. To achieve an effective balance between safety and efficiency in the decision-making process, a motion planning decision strategy tailored for discrete action spaces is developed based on the discrete soft actor-critic algorithm. In response to the challenges posed by the complexity of feature information in dense intersection environments, a multi-level attention mechanism–integrating both feature-level and vehicle-entity-level information–is introduced to significantly enhance feature extraction and processing capabilities. Furthermore, to mitigate the issues of temporal sample distribution imbalance and low utilization of high-value samples in a single experience pool, a dual experience buffer prioritized replay mechanism is proposed, thereby improving training stability. Experimental results indicate that, compared with alternative methods, the proposed framework not only achieves a superior balance between efficiency and safety but also exhibits enhanced interpretability and generalization performance.

The inefficient capture of equipment anomalies has impeded the effective training of models for detecting anomalies in various traffic equipment (TE). This paper proposes an unsupervised knowledge distillation for traffic equipment anomaly detection (UKD-TEAD), eliminating the need for numerous annotations and ensuring the applicability to various equipment anomalies. First, three specialized detection heads based on different object category aspect ratio distributions are designed, to detect multi-scale objects with high precision in complex traffic scenes. Second, a teacher-student model, grounded in hierarchical knowledge distillation, is developed to mitigate the critical feature loss associated with the small size of cropped regions of interest (ROIs). By performing knowledge distillation at different depths of the network, the student network effectively learns the representation capabilities of the teacher network on multiple scale feature layers, thereby improving the anomaly detection performance. Finally, to validate the proposed unsupervised anomaly detection framework, a target detection dataset and an unsupervised anomaly detection dataset were constructed based on traffic inspection data. Experimental results show that the proposed method achieves an [email protected] of 0.862 for TE detection, while the mean area under the curve (mAUC) for anomaly detection reaches 0.857.

Autonomous driving (AD) is critically dependent on intelligent decision-making technology, which is the crucial ingredient in driving safety and overall vehicle performance. And comprehensive consideration of driving heterogeneity, decision synergy, and game interaction is also the cornerstones. Accordingly, this paper constructs a cooperative decision-making framework for autonomous vehicles (AVs) that integrates driving styles within a hierarchical architecture based on deep reinforcement learning (DRL). The upper layer adopts the action shielding mechanism-based dueling-double deep Q-network (D3QN) algorithm incorporating the lane advantages into shared state space to complete the prompt lane-changing (LC) decision, the lower layer applies the soft actor-3-critic (SA3C) algorithm based on the clipped triple Q-learning to provide the continuous speed adaptive control. Three personalized collaborative decision strategies are formulated for particular driving styles in multi-objective optimization preference combined with style-incentive prioritized experience replay (SIPER). The experimental results confirm that the proposed framework can satisfy the personalized driving demands in complex traffic scenarios, effectively explore the prospective LC opportunities, and enhance the driving efficiency by 35.40% with aggressive strategy and the comfort by 56.46% with defensive strategy compared with normal strategy, while maintaining the safety.