Addressing the limitations of current intersection collision warning systems, including non-line-of-sight issues and limited consideration of drivers' characteristics, this paper proposes a cooperative collision warning strategy for connected vehicles at intersections, incorporating driver traits. Firstly, driving behaviors at intersections are categorized into straight and turning, and a turning speed model tailored to driver characteristics is built using the InD dataset. Secondly, vehicle turning trajectory prediction is enhanced with a constant yaw rate model and Extended Kalman Filter, while collision risks are dynamically assessed using a dual-circle vehicle geometry model based on Time Exposed to Risk. Thirdly, a two-level warning strategy grounded in non-cooperative game theory is devised, considering driver heterogeneity and dynamic interactions in unsignalized conflicts. Finally, the strategy is validated through simulations and real-vehicle tests. Results indicate the strategy successfully detected all collisions with a 100% warning rate, reduced collisions by up to 100% among diverse drivers, and decreased accidents by 95.06% and kinetic energy by 52.71% even with aggressive drivers.

In view of the difficulty in designing multi-objective planning algorithms during emergency steering for collision avoidance and the complexity and variability of the number and location of obstacles, a hierarchical decision-making planning algorithm combining sampling and optimization is proposed. Considering environmental and kinematic constraints under structured roads, a variant of the A* algorithm is used to establish the surrounding environmental potential field and compute the kinematic cost using a fifth-degree polynomial. Travel lanes are established based on the coarse trajectories, and the quadratic planning problem is solved using the segmentation-plus-acceleration method to obtain smooth paths so as to ensure comfortable path planning, while guiding the vehicle back to the center of the road. The results of simulation tests and real vehicle tests show that the proposed scheme can flexibly complete the decision planning tasks according to different obstacles and achieve emergency collision avoidance.

To quantify driving risk and develop a safe braking strategy, this paper introduces the concept of Collision Evasion Point (CEP) and builds mathematical models for straight-driving and turning scenarios. Using the CEP, a risk-representation index is defined to quantify driving risk. Moreover, 116 accident cases from China In-Depth Accident Study (CIDAS) database are classified, and the risk representation index is applied to identify high-risk cases. Finally, a dynamic braking strategy based on the braking-time indicator is proposed. Test results show that, across various high-risk scenarios, the proposed risk representation index outperforms Time-to-Collision (TTC) based strategy in identifying scene-level risk, while the braking strategy achieves more reasonable braking times and smoother speed profiles, thereby better avoiding collisions.

In order to overcome the collision and stability issues of the connected vehicle formation in dynamic, uncertain and complex driving scenarios, and improve the driving safety for the connected vehicles, an obstacle avoidance strategy for the connected vehicle formation is proposed basing on optimized artificial potential field method. The obstacle avoidance strategy framework for the connected vehicle formation is designed and the vehicle formation controller basing on the classical artificial potential field method is established. On this basis, the vehicle formation search logic with Levi's flight random search characteristics is proposed to overcome the parameter limitation of the incremental coefficient of attraction and repulsion in artificial potential field method, and enhance the adaptability of the vehicle formation to complex driving environment. The proposed obstacle avoidance strategy is verified by a co-simulation testing platform. Results show that the connected vehicle formation basing on the optimized artificial potential field method can adapt to the complex driving environment more quickly, and has a shorter vehicle formation obstacle avoidance time.

To address the limitations of existing vehicle platooning control methods, such as poor behavior-extendable capabilities, difficulties in handling diverse platoon behaviors during highway driving, and the lack of real-world road testing, this paper proposes a behavior-extendable vehicle platooning control method. Additionally, a leader vehicle acceleration prediction method under packet loss conditions is designed, and a real-vehicle platooning test platform is established. The proposed method is implemented on the real-vehicle platform, and real-vehicle experiments are conducted under packet loss conditions to validate its effectiveness. Road test results, with a maximum speed of 80 km/h and a cumulative distance of approximately 1 000 kilometers, demonstrate that the proposed vehicle platooning control method can safely and effectively manage and extend various platoon behaviors. During stable driving, the average speed errors of the following vehicles are less than 0.62 km/h and 1.55 km/h, respectively, while maintaining stable inter-vehicle spacing. These results verify the effectiveness, real-time performance, and robustness of the proposed control method and the real-vehicle platform.

To balance lane departure issues under various driving styles and visibility conditions, this paper proposes an adaptive lane departure warning strategy. Driving behavior data is collected using driving simulator, and parameters such as lateral position deviation, time-to-lane crossing, and deviation speed are selected. Based on the fuzzy clustering algorithm, drivers are classified according to their driving styles. Subsequently, a Radial Basis Function Neural Network (RBFNN) model is introduced to achieve driving style recognition. Different warning thresholds are designed to construct an adaptive lane departure warning model. Finally, a driver-in-the-loop experiment is conducted. The results indicate that the model achieves an overall accuracy of 94.7%, it can effectively output dynamic thresholds for different drivers and visibility, thereby reducing false alarms and enhancing the applicability of the lane departure warning system.

To address the issue of oil churning losses in the operation of oil-cooled motors, this study proposes a method for calculating oil churning losses in oil-cooled motors. The proposed method integrates fluid mechanics and thermodynamic theory and employs a three-dimensional dynamic CFD multi-physics coupling simulation based on a meshless particle method to calculate oil churning losses. The accuracy of the proposed method is validated through comparative analysis with experimental data, providing valuable insights for optimizing oil-cooled motor design and improving efficiency.