In order to solve the electromagnetic noise issues of Permanent Magnet Synchronous Motor (PMSM) on Electric Vehicle (EV), this paper, based on its electromagnetic noise generation mechanism, proposed a way of reducing electromagnetic radial force by means of test verification to optimize its electromagnetic noise. From the 2 aspects of structural hardware and control strategy, the optimization and improvement were carried out by the verification and comparison approaches such as rotor segmented skew pole optimization, structural stiffness optimization and coupling resonance improvement, current harmonic injection and gap flux density optimization, and each optimization approach was illustrated with practical development case. The result shows that each method has an attenuation of at least 3 dB(A) on electromagnetic noise of PMSM, which further demonstrates the effect and advantage of improving electromagnetic noise by optimizing electromagnetic radial force though experimental means.

In order to improve the communication efficiency and security of Vehicular Ad-hoc NETwork (VANET), this paper proposed an anonymous identity authentication and group key distribution scheme based on quantum key and blockchain. Anonymous credentials for vehicles were generated by a combination of random numbers on the vehicle side and random numbers in the cloud, which achieved privacy protection for the vehicle during authentication. The utilization of blockchain for secure distribution of group keys has been proposed, which reduced the computational overhead of the Quantum Secret Service platform and enabled vehicle revocation and traceability. A two-stage key generation method was devised to ensure the security and efficiency of group key distribution in various scenarios, as well as achieve forward and backward security. The signaling and computation overheads were calculated, the signaling overhead was reduced by nearly half. During the group key distribution process, the computational overhead at the vehicle was reduced by 44%, while the computational overhead at the roadside is approximately 20% of the overhead in the comparison scheme. The formal security analysis results proved the security and feasibility of this scheme.

To realize secure transmission of vehicular network related data and privacy protection, this article proposed a quantum key based identity authentication and data access control scheme for the vehicular networks. An identity authentication scheme and key agreement mechanism based on pre-charge quantum keys were designed, vehicle data access control scheme based on quantum random number generator was proposed to generate quantum encryption keys, allowing the vehicle owner to control access requests for vehicle networking data from external devices to prevent unauthorized access, malicious intrusion by high-privileged personnel and improper opening of vehicle privacy data. Finally, this article conducted security and performance analysis, analysis results show that this scheme has good security, with a computational cost of 0.395 ms and a communication cost of 420 B, which are lower than that of other schemes.

In order to realize identity authentication and key distribution in Internet of Vehicle (IOV) scenario, this paper proposed an enhanced identity authentication scheme for the IOVs based on extended quantum key distribution. The features of this scheme are: (1) Quantum key mobile distribution was completed through quantum security module and preset quantum key in wireless communication, online negotiation of quantum key was completed through Quantum Key Distribution (QKD) equipment in wired communication, to achieve extended quantum key distribution; (2) Basic identity authentication based on post-quantum cryptography encryption and signature algorithms was conducted, and enhanced authentication through preset quantum keys. Finally, through security analysis and performance testing, it is confirmed that this scheme has sufficient security and low computational overhead. The total computational overhead is 1.689 ms, and the performance improvement is 60.43%~70.72%.

Generative Artificial Intelligence (Generative AI), a technology based on neural network models for content generation, has received widespread attention in the industry and academia in recent years. With the continuous deepening of the application of this technology in various fields, the large model based on Generative AI has also a huge impact on the transformation of technical solutions in the field of autonomous driving. This article provided a brief overview of the development of Generative AI technology and large models, including their classification methods and representative models. At the same time, this article also delved into the application of generative models for the field of autonomous driving. Finally, a summary and prospect were conducted in the future development direction of Generative AI technology and autonomous driving technology.

In order to improve the fuel economy of Plug-in Hybrid Electric Vehicle (PHEV), this paper proposed the vehicle speed prediction method based on real-time traffic information. The energy management strategy was established to obtain the optimal fuel economy based on Model Prediction Control (MPC), the real-time optimal torque distribution was optimized with the help of dynamic programming algorithm. Simulation verification was conducted on MATLAB/Simulink platforms which showed that the accuracy was improved by 13.5% compared with traditional vehicle speed prediction methods in real-time road conditions. Compared with the MPC strategy based on historical vehicle speed, the fuel economy of MPC strategy based on real-time traffic information is improved by 9.5%.

A hierarchical control strategy based on Radial Basis Function Neural Network (RBFNN) and Stochastic Dynamic Programming (SDP) was proposed for Plug-in Hybrid Electric Vehicle (PHEV) queue. Firstly, the powertrain structure and mathematical model of PHEV were analyzed in detail, then a hierarchical control framework was constructed. The upper layer adopted RBFNN to train driving data derived from Model Predictive Control (MPC) to generate the speed tracking controller. According to the information of the speed and power demand transmitted from the upper layer, a Markov chain model was established for the lower layer controller, the Markov chain model can realize the optimal energy distribution between PHEV traction battery and the engine based on the SDP theory. The simulation results show that compared with CD/CS strategy and rule-based strategy, the energy consumption of PHEVs in the queue is significantly reduced while ensuring safe driving under high-speed conditions based on the proposed strategy.

For the energy management of new energy vehicles, it is difficult to predict the vehicle speed in a long-term and accurate way, this paper proposed a model-based parametric prediction method to predict the vehicle speed trajectory using the forward-looking data provided by sensors and GPS. Firstly, the speed prediction algorithm based on Intelligent Driver Model (IDM) was established in term of vehicle dynamics and vehicle stop-turn trend; secondly, data was selected from the NGSIM public data set for parameter calibration and simulation; then the algorithm parameters were calibrated using Genetic Algorithm (GA). The results show that the optimized speed prediction algorithm has high accuracy for long-term speed prediction in both unobstructed and congested traffic environments, the error can be controlled in the range of 8%~13%.

For the problems of strong randomness and low training efficiency in intelligent vehicle training under reinforcement learning algorithm, this paper proposed a driving decision framework of intelligent vehicle based on rule constraints and Deep Q Network (DQN) algorithm. The introduced rules were divided into hard constraints related to lane change and soft constraints related to lane keeping, which were implemented by Action Detection Module and reward function respectively. At the same time, the network structure of DQN was improved by combining Dueling DQN and Double DQN, N-Step Bootstrapping learning was introduced to accelerate the training efficiency of DQN. Finally, the effectiveness of the model was verified by comprehensive comparison with the original DQN algorithm in the highway scene of Highway-env platform. The improved algorithm improved the task success rate and training efficiency of intelligent vehicles.

To improve the performance of vehicle detection algorithm based on 3D point clouds, this paper proposed a real-time vehicle target detection algorithm based on the bird’s-eye view of point cloud. First, the original 3D vehicle point cloud was converted into the 2D point cloud RGB feature map. Second, the vehicle’s yaw angle prediction branch was added to achieve accurate vehicle localization based on YOLOv4-tiny network, the target localization capability of the network was improved by adding an improved Spatial Pyramid Pooling-Fast (SPPF) module. Finally, the target detection precision was improved by introducing a dual-attention mechanism and optimizing the loss function in the backbone network. The test results show that the average vehicle detection precision of the proposed algorithm reaches 96.92%, which is 2.94 percentage points higher than YOLOv4-tiny, the average detection accuracy of the proposed algorithm reaches 87.73% in the moderate difficulty of KITTI bird’s-eye view validation set, detection rate reaches 100 frames per second, which is able to meet the real-time requirements.