Vehicles will face different working conditions during the use, and different working conditions have different requirements for vehicle functions, which results in many subdivided models. An off-road vehicle is a subdivision model produced to adapt to complex road conditions. In order to adapt to complex road conditions, vehicles should have a good passing ability, small size, and good flexibility. The six-wheeled vehicle has both good passing ability and small volume, which is the best choice for off-road vehicles. The design of the steering system becomes the key step to improve the flexibility of the six-wheeled vehicle. This paper mainly designs an independent steering system for a six-wheel electric vehicle with higher flexibility. The system is designed for six-wheel electric vehicles driven by six in-wheel motors. It mainly includes mechanical steering system and electronic control steering system. Both mechanical steering systems and electronic control steering system have multiple steering modes. Firstly, this paper introduces the various steering modes of the mechanical steering system and the electronic control steering system. Secondly, a CAD model is established by using the software Solidworks, and the system structure is introduced in detail combined with the CAD model. Finally, a kinematics model is established and calculated. The calculation results showed that the steering system can significantly improve the flexibility of the vehicle, so that the vehicle can complete the steering stably and quickly on complex road sections.

To meet the active avoidance requirements of intelligent vehicles, this paper proposes an efficient hierarchical control system. The upper layer generates a safe avoidance trajectory through an optimized path planning algorithm, while the lower layer precisely controls the vehicle to follow the planned path. In the upper layer design, an improved quintic polynomial method is employed to generate the baseline trajectory. By dynamically adjusting lane change duration and utilizing an improved dual-quintic algorithm, collisions with preceding vehicles are effectively avoided. Additionally, a genetic algorithm is applied to automatically optimize parameters, ensuring both driving comfort and planning efficiency. The lower layer control is based on a three-degree-of-freedom monorail vehicle model and the Magic Formula tire model, employing a model predictive control (MPC) approach to continuously correct trajectory deviations in real time, thereby ensuring stable path tracking. To validate the proposed system, a co-simulation environment integrating CarSim, PreScan, and MATLAB was established. The system was tested under various vehicle speeds and road conditions, including wet and dry surfaces. Experimental results demonstrate that the proposed system achieves a path tracking error of less than 0.002 m, effectively reducing accident risks while enhancing the smoothness of the avoidance process. This hierarchical design decomposes the complex avoidance task into planning and control, simplifying system development while balancing safety and real-time performance. The proposed method provides a practical solution for active collision avoidance in intelligent vehicles.

In order to estimate different road adhesion coefficients, a new method based on the second‐order linear extended state observer is proposed to improve the active safety and driving stability of vehicles. The vehicle single‐wheel driving dynamics model and single‐wheel braking dynamics model are built with analyzing the driving and braking processes of the vehicle. The controllers of ASR and ABS are designed by using sliding mode structure control, and the chattering phenomenon of sliding mode control is eliminated by the saturation function. The road adhesion coefficient can be estimated by the second‐order linear extended state observer in real time with the wheel speeds, and driving torque and braking torque are used as input variables. The simulation results show that the road adhesion coefficient estimation method based on the second‐order linear extended state observer can accurately identify the adhesion coefficient of different roads in the presence of external interference and has strong robustness. At the same time, it also provides a reference for the design of the stability control system of unmanned vehicles.

In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decision-making with a small sample size in actual production processes. The lane-changing decision judgment accuracy rate of the SSA-SVM algorithm model can reach 93.6% compared with the SVM algorithm model without algorithm optimization; the SSA-SVM algorithm model has obvious advantages in decision performance and running speed. Therefore, the proposed new algorithm can effectively solve the problem of the objective consideration of the payoff function in conventional decision game theory.

The problem of stochastic adaptive air-fuel ratio control by the dynamic model of biogas-fuelled engines is investigated in this paper. An adaptive law is employed to estimate the theoretical air-fuel ratio, which is undetermined due to the uncertainty of the methane concentration in the biogas. A stochastic adaptive air-fuel ratio controller in consideration of the stochasticity of the residual gas is designed based on the adaptive law, and the closed-loop system is proven to be mean-square stable. The proposed stochastic adaptive air-fuel ratio controller is validated through a numerical simulation when the theoretical air-fuel ratio is unknown constants and jump signals.