In order to improve the environmental perceive ability and better predict the behaviors of dynamic vehicle targets, a YOLOX model, as the front-end detector, combined with the optimized DeepSort method was proposed to develop the research of dynamic vehicle Multi-Object Tracking. In the process of features matching for dynamic vehicles, the change information of vehicles in the degree of light and shade was extracted to improve the object matching accuracy by adding Haarlike features. Furthermore, based on the application of DeepSort RE-identification network, the improved ResNet13 model was adopted as the backbone network for feature extraction, and SENet was introduced to adjust the feature weight of different channel dimensions. The results show that, compared to the conventional DeepSort method, by using the real on-road video data, the Multi-Object Tracking Accuracy (MOTA) and IDF1 indicator of the proposed algorithm increased by 1.4 percentage points and 7.7 percentage points respectively.

To promote the application of core technologies in the autonomous operation system of intelligent and connected vehicles, this article analyzed the development trends, technical architectures and key technologies of future automotive operation systems from the perspectives of vehicle electronic and electrical architectures. At the same time, based on the application needs of vehicle manufacturers, the paper proposed implementation suggestions to accelerate the production and installation of domestic operation systems and ecological construction, in order to help accelerate the industrialization and development of autonomous operation systems for intelligent and connected vehicles.

For the problem that the autonomous navigation exploration algorithm is easy to fall into the local area, this paper proposed an exploration algorithm combining sampling and deep reinforcement learning. First, the Long-Short-Term Memory (LSTM) network was used locally to obtain the historical pose information of the unmanned vehicle to avoid repeated exploration of the explored area; secondly, the optimal action of the deep reinforcement learning strategy was used to output using deep reinforcement learning and the reward function was designed to encourage the unmanned vehicle to fully explore the unknown area; Finally, the horizontal movement factor of the unmanned vehicle was considered to generate a global exploration path conforming to its current attitude by solving the Asymmetric Travel Salesman Problem (ATSP). In the 2 000 s mine tunnel simulation environment, compared with the Technologies for Autonomous Robot Exploration (TARE) algorithm, the proposed algorithm increased the exploration area by 346.3 m² and reduced the total driving distance by 209.4 m; in the real scene test, the exploration algorithm completed the exploration of the underground garage with an area of 3 444.3 m² and returned to the starting point in 1 014 s and built the environment map.

To make autonomous lane change for intelligent vehicles in the situations with no lane or unclear lane, this paper proposed a lane change trajectory planning method based on virtual lane lines. Firstly, a virtual lane was constructed to divide the road into lanes parallel to the vehicle orientation, the best lane was determined according to the minimum heading angle of collision-free lane change. Secondly, the lane change end state was determined according to the average vehicle speed of the current lane and the target lane. The lane change trajectory was generated using the quintic polynomial combined with vehicle kinematics and dynamics. The path planning algorithm proposed was added to the intelligent planning decision-making module of the intelligent vehicle for simulation and hardware-in-loop test. The results show that the method realizes the function of autonomous lane changing in the situations without lane and improve the safety and driving efficiency of the vehicle in complex traffic environment.

In order to improve the real-time and accuracy of parking slot detection in automatic parking, this paper proposed a lightweight parking-slot detection algorithm based on collaborative attention and graph neural network. Firstly, This algorithm used a lightweight network structure and the improved MobileNetV3 as the feature extraction network, obtained the location information and feature information of the parking-slot marker points through depthwise separable convolution, combined them to obtain the fused features of the marker points, then constructed a graph network structure to enhance the internal relationship of the parking-slot marker points, and combined the cooperative attention mechanism to integrate multiple attention. Finally, the algorithm was tested on the public parking-slot dataset PS2.0. The results indicate that the detection accuracy is better than the current mainstream algorithm, the average reasoning speed of each frame of image can reach 10.1 ms, with good accuracy and real-time performance.

For the problem that intelligent vehicles lack the driver’s subjective driving operation cognition and the risk cognition of traffic participants, this paper proposed a trajectory planning algorithm based on improved Driver Risk Field (DRF). Firstly, the coordinate transformation from Cartesian coordinate system to Frenet coordinate system was carried out for the position of the vehicle, which visually represented the position information between the vehicle and the road; Secondly, a driver risk field model integrating vehicle stability risk was constructed to perceive the risk of traffic participants from the driver’s perspective; Finally, considering the comfort needs of drivers and passengers, obstacle avoidance operations were performed on the perceived risk potential energy high points based on quintic and quartic polynomial trajectories. The simulation and hardware-in-the-loop test results show that the planned trajectory meets both the obstacle avoidance function and the acceleration constraints, ensuring the safety and comfort of driving.

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.

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 this research, a lightweight authentication scheme was designed based on the quantum communication architecture of the Internet of Vehicle (IoV) cloud network. The authentication process consists of 2 stages: registration and authentication, and 2 rounds of authentication between the vehicle and the IoV cloud platform to ensure the security of the scheme. Test results show that this scheme has computational overhead of only 0.179 ms, and communication overhead of 417 B, which is lower than other 4 schemes, had has high efficiency while ensuring security, therefore it has high applicability for most IoV equipment with low computational amount and low communication volume.

To clarify the discharge breakdown characteristics of drive motor bearing (discharge voltage, discharge current, discharge energy, discharge current density, etc.) and its influencing factor, thus reduce electrical erosion damage, a common mode equivalent circuit with concentrated parameters of a drive motor was established to extract shaft voltage. This paper determined the threshold voltage under the minimum oil film thickness according to the theory of elastic flow lubrication, analyzed the discharge breakdown characteristics of the bearing in combination with the discharge breakdown model, and clarified the variation law of the discharge breakdown characteristics under different rotational speed, temperature and force. The results show that as the rotational speed decreases and the temperature and radial force increase, the discharge breakdown characteristics related characteristics gradually decrease, meanwhile the effect of speed and temperature on the breakdown characteristics of the bearing is significantly higher than that of radial force.