Autonomous vehicles play a crucial role in three-dimensional transportation systems and have been extensively investigated and implemented in mining and other fields. However, the diverse and intricate terrain characteristics present challenges to vehicle traversability, including complex geometric features such as slope, harsh physical parameters such as friction and roughness, and irregular obstacles. The current research on traversability analysis primarily emphasizes the processing of perceptual information, with limited consideration for vehicle performance and state parameters, thereby restricting their applicability in path planning. A framework of traversability analysis and path planning methods for autonomous wheeled vehicles on rigid terrains is proposed in this paper for better traversability costs and less redundancy in path planning. The traversability boundary conditions are established first based on terrain and vehicle characteristics using theoretical methods to determine the traversable areas. Then, the traversability cost map for the traversable areas is obtained through simulation and segmented linear regression analysis. Afterward, the TV-Hybrid A* algorithm is proposed by redefining the path cost functions of the Hybrid A* algorithm through the simulation data and neural network method to generate a more cost-effective path. Finally, the path generated by the TV-Hybrid A* algorithm is validated and compared with that of the A* and Hybrid A* algorithms in simulations, demonstrating a slightly better traversability cost for the former.

To achieve a rapid and stable dynamic response of the drive anti-slip system for distributed electric vehicles on low-friction surfaces, this paper proposes an adaptive acceleration slip regulation control strategy based on wheel slip rate. An attachment coefficient fusion estimation algorithm based on an improved singular value decomposition unscented Kalman filter is designed. This algorithm combines Sage–Husa with the unscented Kalman filter for adaptive improvement, allowing for the quick and accurate determination of the road friction coefficient and, subsequently, the optimal slip rate. Additionally, a slip rate control strategy based on dynamic adaptive compensation sliding mode control is designed, which introduces a dynamic weight integral function into the control rate to adaptively adjust the integral effect based on errors, with its stability proven. To verify the performance of the road estimator and slip rate controller, a model is built with vehicle simulation software, and simulations are conducted. The results show that under icy and snowy road conditions, the designed estimator can reduce estimation errors and respond rapidly to sudden changes. Compared to traditional equivalent controllers, the designed controller can effectively reduce chattering, decrease overshoot, and shorten response time. Especially during road transitions, the designed controller demonstrates better dynamic performance and stability.

Currently, there are three fundamental theoretical issues that need to be addressed in the research field of the Archimedes Spiral Wind Blade (ASWB): (1) the existing description of the generation principle of the ASWB’s surface is qualitative, which needs to be formally described using mathematical tools; (2) in the published literature, no studies were found that attempt to fully describe the surface of the ASWB using mathematical tools; (3) in the published literature, no clear definition of the ASWB can be found. The first and second of the above three problems are relatively easy to solve, whereas the ultimate solution to the third problem requires long-term communication and discussion among researchers from various countries. Therefore, this paper focuses primarily on the first of the above problems, namely, to describe the intermediate process of transforming an irregular planar figure into the 3D surface of an ASWB in a standardized manner using mathematical tools including a polar coordinate system, planar and spatial Cartesian coordinate systems, the curve equation of an Archimedes spiral, differential equations, and so on. For the second problem, this paper proposes an alternative approach: namely, conjecturing that the ASWB surface can be approximated by an infinite number of 3D Archimedes spirals, and an example with a finite number of such spirals is provided. For the third problem, this paper can currently only suggest that the precise definition of the ASWB should be based on a standardized description of the generation principle of the ASWB surface, or, alternatively, on an accurate geometric description of the shape of the ASWB surface. The scientific contribution of this paper lies in proposing, for the first time from a purely geometric perspective (independent of aerodynamics), three fundamental theoretical problems concerning ASWB, along with preliminary alternative ideas toward their solution.

Vehicle trajectory prediction on icy and snowy roads is safety-critical because reduced tyre–road adhesion compresses the set of dynamically feasible manoeuvres and can substantially increase braking distances relative to dry pavement. The objective of this study is to develop a lightweight physics-enhanced predictor that jointly minimises positional error and friction-circle violations on low-friction winter roads, so that short-horizon forecasts remain usable by advanced driver-assistance functions. We propose PE-DNN, a physics-informed multi-layer perceptron that predicts absolute future velocities in the ego frame and activates friction-circle and jerk penalties through a linear warm-up schedule. Unlike prior physics-informed trajectory predictors that apply constraints from the first training epoch on zero-mean residual targets, PE-DNN treats target-space design, constraint scheduling, and mechanistic interpretation of the violation rate (VR) as coupled methodological choices. On the Northeastern Vehicle Trajectory Dataset (NVTD; 289 vehicles, $\mathrm{19,049}$ frames), PE-DNN achieves an average displacement error (ADE) of $2.019$ m and $\mathrm{VR}=3.8\%$, with the lowest ADE–VR pair among retrained learning baselines under identical splits, while trading somewhat higher final displacement error (FDE) and extreme-subset ADE (ADE-Ext) for improved physical feasibility. Ablation and sensitivity analyses show that velocity-space labels and warm-up scheduling are essential design components rather than optional refinements.