In the complex and stochastic traffic flow, ensuring safe driving requires improvements in perception and decision-making. This paper proposed a decision-control method that leveraged the scene perception and understanding capabilities of semantic segmentation networks and the stable convergence strategies of Deep Reinforcement Learning (DRL) algorithms to achieve more accurate and effective autonomous driving decision-control. Perception features obtained from cameras and sensors equipped with a semantic segmentation model were used as input for the intelligent agent. DRL algorithms were employed to update decisions based on reward feedback. Experimental results on the CARLA simulation platform demonstrated that the semantic segmentation network effectively identified obstacles, vehicles, and drivable areas, providing high-quality perception data input for the intelligent agent’s decision-making model. Compared to the original algorithms, the proposed Double Deep Q-Network-Semantic Segmentation (DDQN-SS) and Proximal Policy Optimization-Semantic Segmentation (PPO-SS) increased the reward value by approximately 25% and enhanced driving stability by 14.2% and 28.5%, respectively, enabling more stable and precise decision-control during driving. The method proposed in this paper has better improved the decision-control performance of PPO and DDQN in complex scenarios.

Switched reluctance motors (SRMs) offer several advantages, including a magnet- and winding-free rotor, high mechanical strength, and exceptional output efficiency. However, the doubly salient pole structure and high-frequency switching power supply result in significant torque ripple and electromagnetic noise, which limit the application in the field of new energy vehicles. To address these issues, this paper proposes a direct instantaneous torque control (DITC) strategy based on an optimal switching angle torque sharing function (TSF). Firstly, an improved cosine TSF is designed to reasonably distribute the total reference torque among the phases, stabilizing the synthesized torque of SRM during the commutation interval. Subsequently, an improved artificial bee colony (ABC) algorithm is used to obtain the optimal switching angle data at various speeds, integrating these data into the torque distribution module to derive the optimal switching angle model. Finally, the effectiveness of the proposed control strategy is validated through simulations of an 8/6-pole SRM. Simulation results demonstrate that the proposed control strategy effectively suppresses torque ripple during commutation and reduces the peak current at the beginning of phase commutation.

Vehicle–infrastructure cooperative perception (VICP) extends the sensing capability of single-vehicle systems by integrating multi-source information from onboard and roadside sensors, thereby alleviating limitations in sensing range and field-of-view coverage. However, in complex urban environments, the robustness of such systems—particularly in terms of blind-spot coverage and feature representation—is severely affected by both static and dynamic occlusions, as well as distance-induced sparsity in point cloud data. To address these challenges, a 3D object detection framework incorporating point cloud feature enhancement and spatially adaptive fusion is proposed. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined Squeeze-and-Excitation Network (R-SENet) attention module is integrated into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism operating across pillars and intra-pillar points, enabling adaptive recalibration of critical geometric features. In addition, a Feature Pyramid Backbone Network (FPB-Net) is designed to improve target representation across varying distances through multi-scale feature extraction and cross-layer aggregation. Second, to address feature heterogeneity and spatial misalignment between heterogeneous sensing agents, a Spatial Adaptive Feature Fusion (SAFF) module is introduced. By explicitly encoding the origin of features and leveraging spatial attention mechanisms, the SAFF module enables dynamic weighting and complementary fusion between fine-grained vehicle-side features and globally informative roadside semantics. Extensive experiments conducted on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed approach outperforms several state-of-the-art methods. Specifically, Average Precision (AP) scores of 0.762 and 0.694 are achieved at an IoU threshold of 0.5, while AP scores of 0.617 and 0.563 are obtained at an IoU threshold of 0.7 on the two datasets, respectively. Furthermore, the proposed framework maintains real-time inference performance, highlighting its effectiveness and practical potential for real-world deployment.

Vehicle–infrastructure cooperative perception (VICP) overcomes the sensing limitations and field-of-view constraints of single-vehicle intelligence by integrating multi-source information from onboard and roadside sensors. However, in complex urban environments, system robustness—particularly regarding blind-spot coverage and feature representation—is severely compromised by occlusion (static and dynamic) and distance-induced point cloud sparsity. To address these challenges, this paper proposes a 3D object detection framework incorporating point cloud feature enhancement and spatial adaptive fusion. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined-SENet (R-SENet) attention module is embedded into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism—across pillars and intra-pillar points—to adaptively recalibrate key geometric features. Concurrently, a Feature Pyramid Backbone Network (FPB-Net) is constructed to enhance unified target modeling across varying distances via multi-scale extraction and cross-layer aggregation. Second, a Spatial Adaptive Feature Fusion (SAFF) module is introduced to resolve feature heterogeneity and spatial misalignment. By explicitly encoding feature origins and leveraging spatial attention, SAFF enables dynamic weighting and complementary fusion of fine-grained vehicle-side features and global roadside semantics. Experiments on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving Average Precision (AP) scores of 0.762 and 0.694 at IoU 0.5, and 0.617 and 0.563 at IoU 0.7, respectively. Furthermore, the inference speed satisfies real-time requirements, validating the method’s effectiveness and potential for engineering deployment.