The obtainment of road condition information during driving is extremely important for a driver. However, drivers usually cannot notice multiple information at the same time, which definitely increases certain safety risks. Considering this problem, this paper designs a road information collection plus alarm system based on artificial intelligence to monitor road information. The underlying core algorithm of this system adopts the YOLO v3 network with the best comprehensive detection performance in the end-to-end network. We use this network’s advantage of fast detection speed to optimize on its original basis, and propose to “copy” part of the backbone network to build an auxiliary network, which enhances its feature extraction capability. Further, we apply the attention mechanism to the feature information fusion of the auxiliary network and the backbone network, suppress the invalid information channel, and improve the network processing efficiency. Besides, the training part of the network is optimized, and the mAP (mean Average Precision) is improved by setting the scale that meets the target to be detected. Through the test, the average test accuracy of the optimized network model reaches 84.76%, and the real-time detection speed on the 2080Ti reaches 41FPS. Compared with the previous network, the detection accuracy increases by 5.43% after optimization.

An accurate vehicle driving state observer is a necessary condition for a safe automotive electronic control system. Vehicle driving state observer is challenged by unknown measurement noise and transient disturbances caused by complex working conditions and sensor failure. For the classical adaptive unscented Kalman filter (AUKF) algorithm, transient disturbances will cause the failure of state estimation and affect the subsequent process. This paper proposes an AUKF based on a modified Sage–Husa filter and divergence calculation technique for multi-dimensional vehicle driving state observation. Based on the seven-degrees-of-freedom vehicle model and the Dugoff tire model, the proposed algorithm corrects the measurement noise by using modified Sage–Husa maximum posteriori. To reduce the influence of transient disturbance on the subsequent process, covariance matrix is updated after divergence is detected. The effectiveness of the algorithm is tested on the double lane change and Sine Wave road conditions. The robustness of the algorithm is tested under severe transient disturbance. The results demonstrate that the modified Sage–Husa UKF algorithm can accurately detect transient disturbance and effectively reduce the resulted accumulated error. Compared to classical AUKF, our algorithm significantly improves the accuracy and robustness of vehicle driving state estimation. The research in this paper provides a reference for multi-dimensional data processing under changeable vehicle driving states.

In this study, a double fuzzy control strategy for the parallel hybrid electric vehicle (HEV) is proposed, then based on the genetic algorithm (GA) to get better simulation results, and the results are verified by dynamic programming (DP) optimisation. First, the energy management strategy is established by fuzzy control theory. On this basis, considering braking energy recovery, this study designs a double fuzzy vehicle energy control strategy. A simulation analysis of the above two control strategies is carried out in urban dynamometer driving schedule, and the comparison with the work efficiency of the engine and fuel economy performance, respectively, is made; the simulation results show that the double fuzzy control strategy can effectively improve the HEV performance. In order to make rule base more accurate, this study also uses a GA to optimise the fuzzy control rules of the fuzzy controller. Then the DP is used to optimise the energy control strategy and obtain optimal results. The results verified that the design of fuzzy controllers is correct, and the optimised fuzzy control strategy by GA can improve the work efficiency of the engine and fuel consumption.

Effective bearing fault diagnosis can ensure the safe operation of rotating machinery, which is important for the stable operation of rotating machinery. Feature fusion of multi-sensor data is a feasible method to improve fault diagnosis performance. To accurately detect, localize and identify bearing faults, we propose an attention-based two-stage multi-sensor feature fusion (ATS-MSFF) method for bearing fault diagnosis. The first stage, focuses on feature extraction from sensor itself, which utilizes the Channel-Attention to enhance key features in the sensor's signal. The second stage then focuses on feature fusion between sensors, such that the output of each sensor is endowed with additional critical information provided by other sensors. Experiments conducted on a publicly available PU dataset validate the effectiveness of our approach, achieving a high classification accuracy of 99.58% and maintaining stable performance in the presence of noise interference. In addition, the design of this framework takes into account the flexibility of practical applications and can adapt to different numbers of sensor configurations, providing a new solution for the accurate diagnosis of bearing faults.

Currently, infrared fault diagnosis mainly relies on manual inspection and low detection efficiency. This paper proposes an improved YOLOv3 network for detecting the working state of substation high-voltage lead connectors. Firstly, dilated convolution is introduced into the YOLOv3 backbone network to process low-resolution element layers, so as to enhance the network’s extraction of image features, promote function propagation and reuse, and improve the network’s recognition performance of small targets. Then the fault detection model of the infrared image of the high voltage lead connector is created and the optimal infrared image test data set is obtained through multi-scale training. Finally, the performance of the improved network model is tested on the data set. The test results show that the improved YOLOv3 network model has an average detection accuracy of 84.26% for infrared image faults of high-voltage lead connectors, which is 4.58% higher than the original YOLOv3 network model. The improved YOLOv3 network model has an average detection time of 0.308 s for infrared image faults of high-voltage lead connectors, which can be used for real-time detection in substations.

An antilock braking system (ABS) can ensure that the wheels are not locked during the braking process which is an important system to ensure the safety of braking. Regenerative braking is also a crucial system for hybrid vehicles and helps to improve the cruising range of the car. As such, the coordinated control of a braking system and an ABS is an important research direction. This paper researches the coordinated control of the regenerative braking system and the ABS in the hybrid vehicle based on the composite structure motor (CSM-HEV). Firstly, two new braking modes which are engine-motor coordinated braking (EMCB) and dual-motor braking (DMB) are proposed and the coordinated control model of regenerative braking and ABS is established. Then, for the purpose of optimal operating efficiency and guaranteeing the vehicle brake slip rate, a braking force distribution strategy based on predictive control algorithm is proposed. Finally, the Simulink model is established to simulate the control strategy. Results show that the slip rate can well track the target and ensure the efficient operation of the system. Compared with the normal braking mode, the braking energy recovery rate of EMCB is similar, but it can reduce the fuel loss of the engine during the braking process by 30.1%, DMB can improve the braking energy recovery efficiency by 16.78%, and the response time to track target slip is increased by 12 ms.