This paper overviewed comprehensively the typical Active Collision Avoidance (ACA) systems and technological development, it firstly introduced the categories of ACA systems and its technical principle, then summed up the core key technologies of ACA system and technological level and summarized the mass-produced ACA products. The paper finally analyzed and predicted the development trend of ACA products.

For the defects of traditional A* algorithm in unmanned vehicle path planning in structured road scene, such as multiple twists and turns of search path, close to obstacle boundary, unsmooth and exponential growth trend of search time with the increase of grid scale, this paper proposed an improved A* algorithm. Firstly, the map preview module was used to extract the key nodes in the grid map, then the collision field model based on the safe distance was introduced to adjust the cost function. The algorithm conducted incremental extended search based on the information of key nodes until the target node was identified. Finally, the generated path was smoothed by quasi uniform cubic B-spline curve to obtain the final planned path. The simulation results show that compared with the traditional A* and weighted-A* algorithm, the improved A* algorithm proposed in this paper improves the search efficiency, path security and feasibility.

To solve the problems of matching omission and poor real-time performance of existing visual place recognition methods in scenes with changing viewpoints and environments, this paper proposed visual place recognition method based on one level feature fused with coordinate attention. Firstly, the relative place information of features was captured by coordinate attention. Secondly, an encoder for multi-scale feature fusion was constructed using dilated convolution and NetVLAD. Finally, the network was trained based on triplet loss. Validated by Pitts30k and Nordland datasets, the proposed method achieves the same recall accuracy and 19% faster retrieval speed compared with the state-of-the-art method Patch-NetVLAD of the same baseline in the test of position recognition. In the test of loop detection, the proposed method achieves a reasonable balance between robustness and retrieval speed.

For the problems of adaptive cruise control technology, including insufficient environmental adaptability of control algorithm for Deep Reinforcement Learning (DRL), poor model mitigation and generalization ability, this paper proposed the Soft Actor-Critic (SAC) control algorithm based on the principle of maximum entropy and stochastic off-line policy. SAC network was built to fit action value function and action policy function, and auto-adjusting temperature coefficient was used to improve the environmental exploration ability of intelligent agent. For the problem of sparse reward, the reward function was designed by using the idea of reward shaping. In addition, a new experience replay mechanism was proposed to improve the utilization rate of samples. The proposed control algorithm was simulated and tested in different scenes, and compared with Deep Deterministic Policy Gradient (DDPG). The results show that the algorithm has better model generalization ability and migration effect on real vehicles.

In order to effectively solve the problem of limited amount of downloaded data due to the short travel time of vehicles in the coverage of Road Side Unit (RSU) during high-speed movement, this paper proposed a message transmission strategy of vehicle road cooperation mode based on ant colony algorithm. According to the characteristics that information such as vehicle data can be shared between RSUs, the corresponding heuristic function and the corresponding path pheromone update principle were designed to form multiple vehicle road cooperation communication groups, which increased the amount and types of data transmission in the network and avoid falling into the local optimal solution. SUMO simulation platform was utilized for experimental verification. The results show that, compared with the non-cooperation, Coalition Formation Games (CGS) and Multilevel Hyper-graph Partitioning Based on Heavy Edge Matching Scheme (MHEMs), the proposed strategy is better than the above strategies in terms of information transmission volume, road network revenue and operating time, which proves the effectiveness of this strategy.

In order to detect the driver’s fatigue driving situation in time, this paper proposed a yawn detection method based on Dlib and variant Transformer. First, the yawn feature matrix of the driver’s eyes and mouth was constructed based on the face key point model of Dlib. Then a variant Transformer model was proposed in the field of video detection, to extract the yawn feature matrix and classify the results. Finally, it was verified based on the YawDD dataset. The results show that the yawn detection accuracy of the proposed algorithm is 96.8%, which is higher than the existing algorithms, and is suitable for the detection of yawning behavior when the driver is fatigued.

Based on the actual operating data of electric vehicles, this paper proposed an analysis method of in-service power battery usage behaviors, so as to quantitatively evaluate the charging behaviors and driving behaviors of vehicles, and provide effective support for battery fault diagnosis. Firstly, characteristic parameters of power battery use behaviors based on membership function were extracted, and then the accumulative risk score of using behaviors was defined and calculated. Finally, the battery use behavior differences between vehicles and vehicles in different time dimensions were quantitatively analyzed by using the idea of horizontal and vertical comparison. Experimental results show that there is a strong positive correlation between the battery using behavior score quantified in this paper and battery pack consistency, which can fully evaluate the using behavior of power battery, and provide data support for battery fault diagnosis.

In order to improve the energy utilization rate of composite power pure electric logistics vehicle, this paper optimized the existing composite power energy management strategy combined with the road slope. Firstly, a fuzzy controller which can automatically adjust the output power of supercapacitor based on different road slope coefficients was designed. Then Particle Swarm Optimization(PSO) algorithm was used to optimize the fuzzy controller. Finally, MATLAB/Simulink was used in CYC_UDDS to set different slopes for simulation verification under urban road conditions. The simulation results show that the proposed control strategy can reduce the energy consumption by 3.47%, 28.91% and 3.94% respectively on the fixed uphill, fixed downhill and the actual urban road slope, so it can give better play to the advantages of supercapacitor.