Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length.

The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning.

Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.

In order to address the significant nonlinear dynamic characteristics and limited trajectory tracking accuracy of unmanned vehicles under cornering conditions, this paper proposes a trajectory tracking control strategy based on Adaptive Model Predictive Control (AMPC). First, to enhance the accuracy of the vehicle model, an 11-degree-of-freedom vehicle dynamics model is established, incorporating pitch, roll, yaw, rotation around the Z-axis, and wheel-axis rotation. The vehicle motion equations are derived using Lagrangian analytical mechanics. Meanwhile, the tire model is optimized by accounting for the influence of vehicle attitude changes on tire mechanical properties. Based on the principles of nonlinear model predictive control (NMPC) and adaptive control, the AMPC algorithm is developed, its prediction model is constructed, and appropriate control constraints are defined to ensure improved accuracy and stability in trajectory tracking. Finally, simulations under double-lane-change and serpentine driving conditions are conducted using a co-simulation platform involving Carsim and Matlab/Simulink. The results demonstrate that the proposed controller achieves high trajectory tracking accuracy, effectively suppresses vehicle yaw, pitch, and roll motions, and enhances both the smoothness of trajectory tracking and the overall dynamic stability of the vehicle.

Traffic sign detection is a core function of autonomous driving systems, requiring real-time and accurate target recognition in complex road environments. Existing lightweight detection models struggle to balance accuracy, efficiency, and robustness under computational constraints of vehicle-mounted edge devices. To address this, we propose a lightweight model integrating FasterNet, Efficient Multi-scale Attention (EMA), Bidirectional Feature Pyramid Network (BiFPN), and Group Separable Convolution (GSConv) based on YOLOv8s (FEBG-YOLOv8s). Key innovations include reconstructing the Cross Stage Partial Network 2 with Focus (C2f) module using FasterNet blocks to minimize redundant computation; integrating an EMA mechanism to enhance robustness against small and occluded targets; refining the neck network based on BiFPN via channel compression, downsampling layers, and skip connections to optimize shallow–deep semantic fusion; and designing a GSConv-based hybrid serial–parallel detection head (GSP-Detect) to preserve cross-channel information while reducing computational load. Experiments on Tsinghua–Tencent 100K (TT100K) show FEBG-YOLOv8s improves mean Average Precision at Intersection over Union 0.5 (mAP50) by 3.1% compared to YOLOv8s, with 4 million fewer parameters and 22.5% lower Giga Floating-Point Operations (GFLOPs). Generalizability experiments on the CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB) validate robustness, with 3.3% higher mAP50, demonstrating its potential for real-time traffic sign detection on edge platforms.

Advanced driving algorithms, control strategies, and their optimization in self-driving vehicles in various scenarios are hotspots in current research; 4WS-4WD (four-wheel steering and four-wheel drive) is another hotspot in the study of new concept models; and the nonlinear dynamic characteristics of self-driving vehicles (AVs) are prominent in the fast cornering mode, which leads to a significant reduction in the accuracy and stability of trajectory tracking. Based on these research backgrounds, this paper proposes a control strategy optimization idea based on the 4WS4WD vehicle and its optimization model. The main content includes the establishment of a 3D vehicle model that takes into account vehicle load transfer and position change, and the establishment of a hierarchical control strategy based on the optimized NMPC and 4WS4WD models. The controller consists of two parts: an upper tracking controller based on the new vehicle model and NMPC, and a lower decoupled controller. The tracking control effect of the algorithmic control strategy based on the model and controller is validated in the high-speed serpentine motion mode and double-shift linear motion mode on the joint simulation platform of Car Sim and Simulink.