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.

In order to solve the problem that the traditional content popularity prediction method in the Internet of Vehicles cannot accurately capture the vehicle request characteristics and leads to the low cache hit rate, an edge collaborative caching strategy based on federated learning and reinforcement learning is proposed. This strategy pre-caches content with a higher probability of vehicle requests in other vehicles or roadside units to improve the cache hit ratio and reduce the average content acquisition delay. The federated learning method is used to train and predict the content popularity using private data distributed across multiple vehicles, and then the reinforcement learning algorithm is used to solve the objective function to obtain the best cache location for the popular content. The results show that the proposed strategy is better than other caching strategies in terms of cache hit ratio and average content acquisition delay, which effectively improves the performance of the edge cache of the Internet of Vehicles.

In order to improve the confidentiality of message authentication in vehicle-mounted ad hoc networks, an efficient certificateless hybrid signcryption scheme with provable security is proposed. Based on the model of the vehicle-mounted ad hoc network system, a pseudonymous self-generation algorithm is introduced after the vehicle is registered, and a hybrid signcryption calculation method is adopted in the signcryption algorithm. Through theoretical proof and experimental verification, compared with the existing certificateless signcryption scheme, the proposed scheme not only protects the privacy information of the vehicle, but also reduces the computation cost of the trusted center and the roadside unit, and keeps the time overhead and communication overhead at a low level, which proves the unforgeability and confidentiality of the proposed scheme in the random oracle model, and can resist various attacks.