Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods.

In a 3D gaze tracking system, system-calibration is used to determine the positions of the light sources and the screen in camera coordinate system, which plays an important role in the estimation of the 3D gaze and the point of regard (PoR). The traditional way for the system-calibration is the mirror-based method, in which, a mirror is used to make the light sources and the screen can be captured by the camera through specular reflection. However, the mirror-based method is imprecise and complex. In this paper, a novel system-calibration method for the 3D gaze tracking system is proposed. On the basis of the system camera, a calibration camera is placed on the opposite of the screen to constitute a multi-camera system. Images are captured by the two cameras to estimate the light sources and the screen of the system camera coordinate frame. Simulations and experiments were carried out to illustrate the validation of the method. This method solves the problem that the light sources and the screen are not in view of the system camera. In addition, it is easy to implement while ensuring high accuracy and providing a new method for the other similar calibration systems.

Stability prediction of the securing system for autonomous trucks is an important prerequisite for achieving safety monitoring of large cargo transportation and improving logistics efficiency. Considering the side slide risk of large cargo and the inability to predict stability using the existing under-constrained friction securing model, this paper proposes a new vehicle–cargo securing model based on the 6-SPS parallel mechanism. By establishing an analytical 3-DOF model, the dynamics performance of the vehicle–cargo system is analyzed based on the response solution under sinusoidal excitations. To verify the correctness of the analytical model, a multi-body dynamics model of the whole vehicle–cargo system based on the three-dimensional geometric model and the 6-SPS parallel mechanism is established for simulation in ADAMS. According to road class, pavement roughness is modeled by a white noise power spectrum method as the excitation in the simulation. The results show that the dynamics response of the analytical model accords well with that of the simulation model, with relative errors of 8.34% and 0.036% in amplitude and frequency, respectively. The proposed method can provide theoretical support for accurate stability prediction and for achieving safety monitoring of large cargo transportation for autonomous trucks.

To contribute to the development of sustainable transport that is safe, eco-friendly, and efficient, this research proposed a safe and ecological speed control system for heavy-duty vehicles on long–steep downhill and sharp-curved roads under a partially connected vehicles environment consisting of connected heavy-duty vehicles (CHDVs) and conventional human-driven vehicles. This system prioritizes braking and lateral motion safety before improving fuel efficiency and ensuring traffic mobility at optimal status, and optimizes the speed trajectories of CHDVs to control the entire traffic. Speed optimization is modelled as an optimal control problem and solved by the iterative Pontryagin’s maximum principle algorithm. The simulation-based evaluation shows that the proposed system effectively reduces the peak temperature of the brake drums, the lateral slip angle of the vehicle wheels, and the lateral load transfer rate of the vehicle body; all these measurements of effectiveness are limited to safe ranges. A detailed investigation reveals that the proposed system reduces fuel consumption by up to 15.49% and inhibits the adverse effects on throughput. All benefits increase with the market penetration rate (MPR) of CHDVs and the traffic congestion level and reach significant levels under low MPRs of CHDVs. This indicates that the proposed system has good robustness for the impedance from conventional vehicles and could be implemented in the near future.

This study provides insights into the building of environmentally and socially sustainable and livable cities by investigating commuter buses’ vehicle speed and clearance, considering two traffic scenarios (working days and weekends) and two weather conditions on urban roads. Thirty participants drove ten different routes during natural driving. The drivers were observed under the following traffic conditions: free flow (Grade I), steady flow (Grades II and III), unsteady flow (Grade IV), working days and weekends, and sunny and heavy snow weather. A method for collecting accurate traffic flow data was developed using a radar sensor to measure the real-time distance between vehicles. The real-time vehicle spacing and speed were detected using a radar sensor and a mobile app, respectively. The results showed that speed decreased obviously from 11.2% to 16.5% on working days compared to a similar situation on weekends, especially in heavy snow weather (from 33.8% to 40.7%). The lowest average speed was obtained in the traffic environment Grade IV. Commuter buses maintained a minimum vehicle clearance during working or heavy snow days in traffic environment Grade IV. Policymakers should consider the insights of this study to develop new, dynamic commuter schedules under diverse conditions.