Pavement defect detection is of profound significance regarding road safety, so it has been a trending research topic. In the past years, deep learning based methods have turned into a key technology, with advantages of high accuracy, strong robustness, and adaptability to complex pavement environments. This paper first reviews the methods based on image processing and 3D imaging. As for image-based processing techniques, they can be classified into three types based on how to label the collected data: fully supervised learning, unsupervised learning, and other methods. Different methods are further classified and compared with each other. Second, the pavement detection methods based on 3D data are sorted out, thereby summarizing their benefits, drawbacks, and application scenarios. Third, the study proposed the major challenges in the field of pavement defect detection, introduced validated datasets and evaluation metrics. Finally, on the basis of reviewing the literature in pavement defect detection, the promising direction is put forward.

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To address this issue, this study proposes a foggy weather detection method based on the YOLOv5s framework, named YOLOv5s-Fog. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer called SwinFocus. Additionally, the decoupled head is incorporated into the model, and the conventional non-maximum suppression method is replaced with Soft-NMS. The experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model, YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

In extreme off-road scenarios, autonomous driving technology holds strategic significance for enhancing emergency rescue capabilities, reducing labor intensity, and mitigating safety risks. Challenges such as adverse conditions, complex terrains, unstable satellite signals, and lack of roads pose serious safety challenges for autonomous driving. This perspective first delves into a Bird's Eye View (BEV)-based perception-planning framework, aiming to enhance the adaptability of intelligent vehicles to their environment. Subsequently, this perspective further discusses key issues such as Cyber-Physical-Social Systems (CPSS), foundation models, and technologies like Sora for off-road scenario generation, vehicle cognitive enhancement, and autonomous decision-making. Ultimately, the discussed framework is poised to endow intelligent vehicles with the capability to perform challenging tasks in complex off-road scenarios, realizing a more efficient, safer, and sustainable transportation system, thereby providing better support for the low-altitude economy

As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, making it difficult to achieve stable and precise leader-following. This paper decouples the leader-following control into speed and curvature control to address such issues. It utilizes model reference adaptive control to establish reference models for the speed and curvature subsystems and designs corresponding parameter adaptive control laws. This control method enables the actual vehicle speed and curvature to effectively track the response of the reference model, thereby addressing the impact of frequent changes in the steering resistance coefficient. Furthermore, this paper demonstrates significant improvements in leader-following performance through a series of simulations and experiments. Compared with the traditional PID control method, the results shows that the maximum following distance has been reduced by at least approximately 12% (ensuring the ability to keep up with the leader), the braking distance has effectively decreased by 22% (ensuring a safe distance in an emergency braking scenario and improving energy recovery), the curvature tracking accuracy has improved by at least 11% (improving steering performance), and the speed tracking accuracy has increased by at least 3.5% (improving following performance).

Multi-axle electric heavy-duty trucks face significant challenges in maintaining braking stability and achieving real-time control during regenerative braking due to their large mass and complex inter-axle coupling dynamics. To address these issues, this paper proposes an improved model predictive control (IMPC) strategy that enhances computational efficiency and control responsiveness through an instantaneous response mechanism. The approach integrates a first-order error attenuation term within the MPC framework and employs an extended Kalman filter to estimate tire–road friction in real time, enabling adaptive adjustment between energy recovery and stability objectives under varying road conditions. A control barrier function constraint is further introduced to ensure smooth and safe regenerative braking. Simulation results demonstrate improved energy recovery efficiency and faster convergence, while real-vehicle tests confirm that the IMPC maintains superior real-time performance and adaptability under complex operating conditions, reducing average computation time by approximately 14% compared with conventional MPC and showing strong potential for practical deployment.