To address the issue of deviation in the estimation of vehicle mass and road gradient by applying the longitudinal dynamics model when there is a lateral component force in the driving process of vehicles, this paper proposes a coupled mass estimation model based on longitudinal-horizontal dynamics and an algorithm for slope estimation. By analyzing the effect of the acceleration phase on mass estimation, the mass estimation trigger condition is set and the recursive least squares method with forgetting factor is used to estimate the vehicle mass, and the kinematic Kalman filter is fused with the kinematic extended Kalman filter to jointly estimate the road slope. The algorithm is validated by Simulink-CarSim joint simulation and real vehicle test. The results show that the error of the mass estimation algorithm based on longitudinal-transverse dynamics is 0.82%, and the error of the fusion slope estimation algorithm is within 3%, which verifies that the algorithm has good accuracy and real-time performance.

To establish an effective electromagnetic radiation simulation prediction and design capability during the vehicle design phase, this paper investigates the generation mechanism of electromagnetic radiation from power cables in the motor drive system based on electromagnetic wave radiation theory, and proposes a vehicle finite element model for electromagnetic compatibility simulation. An electromagnetic interference model is built to simulate and predict the electromagnetic radiation emitted by the power cables. The accuracy of the model is validated by comparing the simulation results with actual measurement data. Utilizing this simulation model, the cable layout is optimized, thereby reducing the intensity of interior electromagnetic radiation. The findings indicate that this approach can identify the electromagnetic radiation risks associated with high-voltage cable routing during the design phase, thus avoiding costly modifications in the prototype testing stage. Furthermore, this method contributes to shortening the vehicle design and development cycle while reducing testing costs.

In order to realize the development of vehicle functional safety based on Service Oriented Architecture (SOA), the forward development process for automotive functional safety based on SOA is established combining with the ISO 26262 standard. The functional safety design methods of function-based concept phase and architecture-based system development are proposed. On the one hand, for the concept development phase, the functional safety requirements are derived based on the product capabilities, and the function safety class of product capabilities is defined. On the other hand, for the system development phase, the technical safety requirements are derived and distributed based on the software component architecture and physical deployment.

Under high-frequency variable loads, EV electrical system is prone to causing bus voltage oscillations and instability. A small-signal model of the vehicle’s electrical system has been established, obtaining the output impedance of the system source-transformer and the input impedance of the motor load. Based on the traditional impedance ratio criterion and combined with the system’s stability margin, the stability determination principle of the vehicle’s electrical system has been derived. By using a Bode diagram for predicting system instability, research is conducted to explore the instability boundary of pure electric vehicle under high-speed conditions. The simulation verification results show that the vehicle’s maximum speed shall not exceed 188 km/h, and torque variation of hub motor will affect vehicle stability, the torque mutation of single hub motor shall not exceed 220 N·m, which is basically consistent with theoretical analysis results. The results prove that this stability determination principle can predict instability boundary of vehicle electrical system to some extent.

In addressing the issues of suboptimal solutions and poor exploration performance in narrow passages of intelligent car path planning using the Rapidly-exploring Random Tree-Connect (RRT-Connect) algorithm, this paper improves the RRT-Connect algorithm in expansion strategy and path smoothing based on an analysis of the basic principle of the RRT-Connect algorithm. Firstly, in terms of expansion strategy, a probability bias method is introduced to screen random points, and an expansion method based on artificial potential fields is used to shorten paths and reduce computation time. Secondly, regarding path smoothing, a third-order B-spline curve is introduced to optimize the path and generate a smooth path, ensuring that the path meet the dynamic characteristics of intelligent cars. Finally, the superiority of the improved RRT-Connect algorithm is demonstrated through comparative simulation. The results show that in environments with simple obstacles, complex obstacles and narrow paths, the average time and path length of the improved RRT-Connect algorithm are superior to those of the traditional RRT-Connect algorithm.

The large number of parameters in deep learning models for driver distraction detection makes it difficult to deploy them on embedded devices. To address this issue, this paper proposes a lightweight distracted driving detection algorithm, YOLOv8n-SGC, based on YOLOv8n. First, a lightweight backbone network, ShuffleNetV2, is constructed, and Ghost convolution is introduced to reduce the number of model parameters and computational cost, achieving model lightweighting. Second, a Convolution and Attention Fusion Module (CAFM) is added after the backbone network to fuse global and local features and improve the algorithm’s detection accuracy. The results show that the improved algorithm model has a reduction in parameters and computational cost compared to the benchmark model, a 28.67% reduction in volume, a 41.79% reduction in inference time, and an mAP increase of 1.1 percentage points.

In order to solve the problem of target detection failure of millimeter wave radar due to radar occlusion under complex road conditions, this paper proposes a radar occlusion detection method based on spectral estimation. Multi-channel signal accumulation is used to solve the two-dimensional spectrum of echo, adaptive CFAR detection algorithm is used to eliminate the radar target, and Spectrum estimation is used to solve the echo background power. The occlusion detection is accomplished by counting the energy distribution characteristics of the echo in a certain time interval. The experimental results show that the proposed method can achieve the radar occlusion state estimation with an accuracy of over 70% in 10 s and over 90% in 100 s.