In order to improve the quality and reliability of in-vehicle communication, in-vehicle visible light communication technology is introduced by analyzing the influence of infrastructure-vehicle channel transmission model and road infrastructure parameters. Based on the line-of-sight channel between the infrastructure and the vehicle, a model is established to analyze the communication loss during signal transmission. Closed-form analytical equation of the system performance are derived by using the line-of-sight communication Bit Error Rate (BER), downtime probability, and the Block Error Rate (BLER). The effect of the composite pointing error on BER, downtime probability, and BLER of the line-of-sight communication system is analyzed. The experimental results show that: under the no-fading and line-of-sight transmission conditions, increase the height of the street light reduces the BER, while increasing the communication link distance increases the BER; raising the downtime threshold reduces the downtime probability of the line-of-sight link; increasing in the number of transmitted bit blocks helps to improve the performance of BLER; when the number of BER blocks is increased, the system’s BLER decreases rapidly; after considering the pointing errors, BER, downtime probability and BLER all increase, which indicates that pointing error has a debilitating effect on the system communication.

A three-segment Pseudo-Random Frequency Active Zero State Pulse Width Modulation (PRF-AZSPWM) strategy based on Markov process is proposed to address the common mode interference problem in electric vehicle electric drive systems. Based on the equivalent zero vector effect, this strategy introduces a binary Markov chain to generate random synthetic signals, allowing the carrier frequency to vary randomly within a certain range, while selecting appropriate transmission probabilities to spread the harmonic energy to a wider frequency range. The simulation results show that the proposed scheme effectively reduces the common-mode interference level of the electric drive system, and the effectiveness of the proposed strategy is further confirmed through prototype testing.

To weaken the vibration and noise of automotive synchronous motors, this paper proposes a combined rotor slotting design scheme and electromagnetic noise forward optimization design method. Firstly, the mechanism of electromagnetic vibration noise is explored, then based on Maxwell tensor method and finite element method, the time-space distribution characteristics of radial electromagnetic force wave are studied, and the main electromagnetic force harmonic components causing electromagnetic noise are determined. Secondly, an improved design scheme of combined rotor slotting is proposed, and the optimal solution of structural parameters of slotting scheme is determined by combining the optimal prediction meta-model and strength Pareto evolutionary algorithm. Finally, the electromagnetic simulation model of the motor is established, and its line back electromotive force, cogging torque and output torque are compared and evaluated. The results show that the rotor slotting design can effectively suppress the spatial 0-order 12f electromagnetic force harmonic amplitude, improve the back EMF waveform, reduce the cogging torque and torque ripple, and thus reduce the vibration noise. Compared with the original prototype, the harmonic amplitude of the 0-order 12f electromagnetic force is weakened by 81.51%, the torque ripple is reduced by 44.98%.

The master cylinder pressure estimation of the Electro-Hydraulic Brake (EHB) system is crucial to reduce the sensor dependence of EHB. In this paper, the master cylinder pressure is estimated based on BP neural network. First, a real-vehicle road test is carried out and data such as vehicle speed, master cylinder piston displacement, master cylinder piston speed and master cylinder pressure are collected. Second, a BP neural network is established using the master cylinder piston displacement and master cylinder piston speed as feature inputs and the real master cylinder pressure as target output. Third, the BP neural network is trained by the training dataset and gradient-descent algorithm. Finally, the pressure estimation performance is verified by the testing dataset. The results show that the proposed algorithm reduces the estimation error by 38% and 15%, compared with the dynamic pressure-displacement model and the LSTM-based estimation algorithm, respectively.

An emergency obstacle avoidance strategy based on domain architecture combining longitudinal and transverse directions is proposed in the scenario of pedestrian crossing the street. Firstly, a pedestrian emergency obstacle avoidance system based on domain architecture is built, and the longitudinal and transverse trajectory decision planning method is fitted through the maximum deceleration model and polynomial group. Based on the sampling discrete numerical calculation method, the optimal trajectory solution is selected by the calculation and comparison of cost function that comprehensively considers the obstacle avoidance safety distance, dynamic constraint and smoothness. Then, the double-loop Proportion Integral Differential (PID) and Linear Quadratic Regulator (LQR) are used to control the longitudinal and transverse movements. Finally, the applicability of the obstacle avoidance system is verified through joint simulation of PreScan, CarSim and Simulink, as well as domain architecture system delay analysis. The obstacle avoidance system improves road safety effectively.

In order to ensure the safety and lateral stability of the vehicles during the collision avoidance process, this paper proposes a collision avoidance trajectory planning and predictive tracking control method for vehicles based on adaptive potential field. Firstly, the vehicle point mass model is established, the adaptive potential field function is designed and the nonlinear model predictive control problem is constructed to solve the locally optimal trajectory. Secondly, the vehicle lateral stability performance is analyzed, the phase plane constraint and indirect constraint are designed, and the trajectory tracking controller is designed based on the model predictive control method to realize the locally optimal trajectory tracking. The joint simulation of CarSim and Simulink verifies that the proposed method can improve the lateral stability performance of the vehicle while avoiding collision. At the meanwhile, the real-time and effectiveness of the method are further proved by real vehicle tests.

To address the issue of collision risk during lane changing of intelligent vehicles caused by changes in the driving status of other vehicles, real-time local path planning is required for the host vehicle. Based on Model Predictive Control (MPC), a lane change trajectory replanning strategy is proposed, which is divided into lane change trajectory correction strategy, lane change reentry strategy, and forward active collision avoidance strategy according to the collision risk. A lateral control based on MPC and a longitudinal control based on dual PID are established, and a comprehensive lateral-longitudinal trajectory tracking controller is designed with the longitudinal speed as the joint point. The trajectory planning module, trajectory replanning module, and trajectory tracking module are integrated in layers, and simulations are conducted to verify the lane change trajectory replanning strategies. The simulation results show that the lane change trajectory replanning and trajectory tracking control based on MPC can achieve safe collision avoidance for vehicles in different scenarios.

In order to address the issue of sensor configuration redundancy in intelligent driving, this paper constructs a multi-objective optimization model that considers cost, coverage ability, and perception performance. And then, combining a specific set of parameters, the NSGA-II algorithm is used to solve the multi-objective model established in this paper, and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints. Finally, using the decision preference method proposed in this paper that combines subjective and objective factors, decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences. The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle, and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.

To address the issue of reduced network schedulability when the shortest path is adopted for all transmission traffic, a flow attribute-aware evaluation function and redundant routing scheduling method for time-triggered flows are proposed. The heuristic algorithm is used to solve the routing scheme with the largest evaluation function, and the integer linear programming is used to solve the scheduling. The simulation experiment results demonstrate that, in the region-oriented electronic and electrical architecture network topology, compared with the K Shortest Path (KSP) and Degree of Conflict (DoC) routing schemes, the proposed scheme enhances the success rate of scheduling by 38.9% and 14% respectively while guaranteeing network reliability, and further validates the effectiveness of this method.

In order to ensure the real-time transmission requirements of the network and improve the success rate of scheduling, an Audio Video Bridging (AVB) stream routing and scheduling algorithm with real-time perception is proposed to simulate the AVB stream transmission in the scenario of vehicle-mounted Time-Sensitive Networking (TSN), and the influence of the proposed algorithm on the success rate of network scheduling is analyzed. The experimental results show that with the increase of the number of data flows, compared with the non-real-time perception algorithm and some real-time perception algorithms, the AVB flow routing and scheduling algorithm with real-time perception increases the success rate of network scheduling by 26% and 11% respectively, and the algorithm can optimize the bandwidth reservation of AVB flow in the TSN network and realize the real-time perception of data flow routing and packet information.