Driving decision-making determines the safety and rationality of autonomous commercial vehicles. Aiming at the issue of safe driving decision-making, herein, a rear anti-collision decision-making methodology based on deep reinforcement learning (RAD-DRL) was creatively proposed. Firstly, aiming at the dynamic coupling of rear anti-collision factors for safe driving, a driving decision network based on an actor-critic framework was proposed for sensor data processing. Then, inspired by the idea of multi-objective optimization, a refined reward function is developed. It comprehensively considers the impact of backward target types, safety clearance, and vehicle roll stability on the rear collision. Finally, the RAD-DRL was trained (with different values of random seeds), tested, and verified in a simulation environment where road network and traffic situations were built-in SUMO (Simulation of Urban Mobility). After 30,000 training episodes, effective and reliable rear anti-collision driving decisions were achieved by our proposed RAD-DRL. The simulated results indicated that the RAD-DRL has remarkable superiority in generalization and effectiveness in expressway scenarios with different driving cases. Especially, it maintained good decision performance in the corner case in which the backward vehicle suddenly accelerates.

The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed into two tasks, and a multitask network based on MobileNet is designed. This algorithm includes two parts: lane line semantic segmentation branch and lane line Id embedding branch. The lane line semantic segmentation branch is mainly used to obtain the segmentation results of lane pixels and reconstruct the lane line binary image. The lane line Id embedding branch mainly determines which pixels belong to the same lane line, thereby classifying different lane lines into different categories and then clustering these different categories. Secondly, the adaptive perspective transformation model is adopted. In this model, the motion information is used to accurately convert the original image into a birds-eye view image, and then the least-squares second-order polynomial fitting is performed on the lane line pixels. Finally, experiments on the CULane dataset show that the proposed method achieved similar or better performance compared with several state-of-the-art methods, the F₁ score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposed method, and then the field experiments results show that the proposed method has good practical application value in various complex traffic scenes.

To realize the effective positioning in urban canyons, an enhanced map-aided Global Positioning System (GPS)/three-dimensional (3D) reduced inertial sensor system (RISS) tightly combined positioning strategy is proposed. First, the 3D RISS is only based on the built-in controller area network (CAN). CAN bus sensor without additional sensors is first constructed to lower the cost. Then, a simple but effective enhanced map is created to assist positioning. Based on the map, a Kalman filtering (KF) tightly coupled method is proposed to fuse the 3D RISS with GPS information and to achieve the preliminary positioning. In KF-based preliminary positioning method, a simply observation noise variance optimization algorithm based on 2D enhanced map is proposed to improve KF method. In this algorithm, the value of the observation noise variance matrix is determined only according to the building plane information which is contained in the enhanced map. Further, a multiweight map matching algorithm is proposed for optimizing the initial positioning results. In this algorithm, factors such as distance, direction, road network topology, and lane change are considered and applied to map matching to further increase the positioning performance and form the final positioning results. Finally, the effectiveness of the strategy is proved by field test. The results show that this method has better accuracy and reliability than the conventional method.

In various applications of automatic underwater vehicles (AUVs), it is necessary to acquire the real-time location and speed information of the AUV. However, the complicated and fluctuating marine environment leads to measurement information loss. The accuracy of noise measurement is vital for accurate state estimation, but it is difficult for traditional algorithms to acquire time-varying noise measurements. Due to inaccurate process models and measurement noise, the filtering performance becomes poor or even diverges. To address the problems above, a switched variational estimation filtering (SVEF) algorithm, which combines the advantages of both Gaussian filtering and the variational estimation (VE) method, is proposed. In SVEF, VE is embedded into a linear or nonlinear filtering algorithm. Owing to the continuous discrete underwater navigation model, the state vector is estimated by the SVEF in the case of measurement information loss, and the accurate position and velocity of the AUV are determined. The experimental results prove that the SVEF achieves better positioning precision and is more robust than other conventional algorithms for AUV applications.

Aiming to the fatigue life prediction of Body-In-White(abbreviated as BIW), taking the road surface measured acceleration power spectral density as an input excitation, the correlations between the load spectrum were analyzed, the frequency response characteristics of BIW was obtained by modal method. According to the frequency response analysis, fatigue life of BIW was computed using fatigue accumulation damage theory, measure improvements of weak area were proposed. On this basis, the sensitivity level on load to the fatigue life, the effects of surface smoothness of material and treatment methods on fatigue life were studied, the results of which had a certain referential value to the optimal design in structure of BIW.