Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles.

Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective of minimizing path tracking error, with the upper controller, we adopted the DDPG method to construct an adaptive look-ahead distance optimizer in which the look-ahead distance was dynamically adjusted in real-time using a reinforcement learning strategy. Meanwhile, reinforcement learning training was carried out with randomly generated paths to improve the model’s generalization ability. Based on the optimal look-ahead distance output from the upper layer, the lower layer realizes precise closed-loop control of torque, required for steering, based on the PP method. Simulation results show that the path tracking accuracy of the proposed method is better than that of the LQR and PP methods. The proposed method reduces the average tracking error by 94.0% and 79.2% and the average heading error by 80.4% and 65.0% under complex paths compared to the LQR and PP methods, respectively.

Accurate vehicle state estimation is a critical prerequisite for electric vehicle motion control, yet its performance is highly sensitive to deviations in inertial parameters. Variations in vehicle mass and moment of inertia caused by changing loads can lead to model mismatch, thereby degrading the accuracy and robustness of state estimation. To this end, this paper proposes a hierarchical collaborative estimation framework that integrates the Maximum Correntropy Adaptive Unscented Kalman Filter (MCAUKF) with a Physics-Informed Neural Network (PINN) for inertial parameter identification and key state estimation in electric vehicles. The upper layer employs MCAUKF for robust online identification of unknown inertial parameters, such as vehicle mass and moment of inertia. The lower layer develops a PINN-based state estimator that incorporates physical constraints by embedding the coupled dynamic residuals of longitudinal, lateral, and roll motions into the supervised learning process, thereby enabling high-precision real-time estimation of key dynamic states, including yaw angle, longitudinal velocity, and roll angle. Simulation results demonstrate that the proposed method can effectively achieve coordinated estimation of inertial parameters and key states under varying load conditions and complex maneuvering scenarios, significantly improving overall estimation accuracy and robustness.

Detecting small unmanned aerial vehicles (UAVs) from ground-based sensors is challenging because distant targets occupy few pixels, provide weak visual cues, and are easily confused with complex backgrounds under illumination changes. Existing radar–vision detectors have incorporated radar cues at different representation and prediction stages; however, these interactions are typically designed for stage-specific objectives, while the use of a common target-location reference across representation and optimization stages remains insufficiently explored. We develop URPG-ML, a radar-prior-guided extension of RT-DETR that uses a shared image-plane radar prior as a common spatial reference from which stage-specific guidance is derived. Sparse radar observations are projected onto the image plane and spatially diffused to construct this prior; these operations establish spatial alignment and are not treated as the principal methodological contribution. The resulting prior is adapted to three distinct learning stages: radar-conditioned channel modulation for within-level representation, prior-conditioned adaptive scale aggregation for cross-level interaction, and reweighting of matched-target supervision. In this way, radar-derived spatial support is reused with consistent target-location semantics across multilevel representation learning and set-based detection optimization. On the public Multi-Modal Anti-UAV Dataset, URPG-ML achieves 95.4% mAP@0.5, 49.9% mAP@0.5:0.95, and 49.1% small-object average precision (APs). Compared with YOLOv11-dualBackbone, URPG-ML improves Recall, mAP@0.5:0.95, and APs by 6.1, 7.9, and 7.8 percentage points, respectively. Repeated ablation experiments over five random seeds further show that the principal performance trends are maintained across different training runs, while fine-grained and sensitivity analyses support the complementary roles of the stage-specific uses of the shared radar prior. These results demonstrate that stage-specific utilization of a common radar spatial reference provides an effective strategy for improving weak small-UAV representation, localization, and detection performance.