Vehicle mass is crucial to autonomous vehicles control. Affected by the nonlinearity of vehicle dynamics between vehicle states, it is still a tough issue to estimate vehicle mass precisely and stably. The transformer aided adaptive extended Kalman filter is proposed to further improve the accuracy and stability of estimation. Firstly, the transformer-based estimator is introduced to provide an accurate pre-estimation of vehicle mass, with the nonlinear dynamics among vehicle states being learned. Secondly, on the basis of comparing the real-time input and training data of neural network, the weight adjustment module is designed to present an adaptive law. Finally, the adaptive extended Kalman filter is proposed to meet the demand of accuracy and stability, where the pre-estimation of transformer-based estimator is integrated with the adaptive law. Dataset is collected by conducting heavy-duty vehicle simulation. The mean absolute percentage error, mean absolute error, root mean square error and convergence rate averaged over simulation tests are 0.90%, 256.47 kg, 357.01 kg and 184 steps, respectively. The results show the outperformance of the proposed method in terms of accuracy and stability.

Traffic accidents, which cause loss of life and pollution, are a social concern. The complex traffic environment on mountain roads increases the harm caused by traffic accidents. This study aimed to identify safety-critical events related to accidents on mountain roads to understand the causes of the accidents, improve traffic safety, and protect the environment. In this study, a naturalistic-driving data collection system, consisting of approximately 8000 km of naturalistic-driving data from 20 drivers driving on mountain roads, was developed. Using these data, a comparative analysis of the identification performance of the support vector machine (SVM), backpropagation neural network (BPNN), and convolutional neural network (CNN) methods was conducted. The SVM was found to yield optimal performance. To improve the identification performance, the yaw rate and information entropy of the data were added as input variables. The improved SVM method yielded an identification accuracy of 90.64%, which was approximately 15% higher than that yielded by the traditional SVM. Moreover, the false positive and false negative rates of the improved SVM were reduced by approximately 10% and 20%, respectively, compared with the traditional SVM. The results demonstrated that the improved SVM method can identify safety-critical events on mountain roads accurately and efficiently.

Real-time collision risk prediction is essential for improving highway safety and reducing traffic accidents. However, previous studies have mainly used crash data and associated spatially discrete and temporally continuous traffic data, overlooking the potential of vehicle trajectory data, which provides comprehensive spatio-temporal information to characterize traffic near a specific location. Moreover, researchers have typically focused on either traffic flow characteristics or inter-vehicle microscopic kinematic characteristics for real-time risk prediction, with a dearth of studies integrating these two aspects. Given that risk events transpire more frequently than accidents and exhibit a strong correlation with them, it is imperative to concentrate more on risk events to proactively diminish crash probabilities. This study introduces a novel approach that extracts traffic flow and inter-vehicle kinematic features from risk events. It also provides a comparative analysis of the effectiveness of five machine-learning methods (Logistic Regression, K-Nearest Neighbors, eXtreme Gradient Boosting, Random Forests, and Multilayer Perceptron) and two data-processing strategies (oversampling and undersampling) in addressing risk identification and prediction issues. The results showed that (1) the synergistic use of traffic flow and inter-vehicle kinematic features surpasses the use of a single feature in identifying and predicting risks; (2) The eXtreme Gradient Boosting model, trained on the undersampled dataset, emerges as the optimal model for risk identification, boasting an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.976 and an F1 score of 0.604; (3) The RF model exhibits commendable performance under both risk prediction conditions (5 s ahead prediction and 10 s prediction), demonstrating the highest performance with F1 scores of 0.377 and 0.374, respectively. Additionally, it was discovered that the resampling strategy does not always prove effective in developing risk analysis models and should be chosen based on the model’s characteristics and target metrics. This offers valuable insights into the selection of data-processing strategies when handling unbalanced data. Finally, the study’s limitations and potential enhancements are discussed.

Light Detection and Ranging (LiDAR), a laser-based technology for environmental perception, finds extensive applications in intelligent transportation. Deployed on roadsides, it provides real-time global traffic data, supporting road safety and research. To overcome accuracy issues arising from sensor misalignment and to facilitate multi-sensor fusion, this paper proposes an adaptive calibration method. The method defines an ideal coordinate system with the road’s forward direction as the X-axis and the intersection line between the vertical plane of the X-axis and the road surface plane as the Y-axis. This method utilizes the Kalman filter (KF) for trajectory smoothing and employs the random sample consensus (RANSAC) algorithm for ground fitting, obtaining the projection of the ideal coordinate system within the LiDAR system coordinate system. By comparing the two coordinate systems and calculating Euler angles, the point cloud is angle-calibrated using rotation matrices. Based on measured data from roadside LiDAR, this paper validates the calibration method. The experimental results demonstrate that the proposed method achieves high precision, with calculated Euler angle errors consistently below 1.7%.

In this paper, a planning method based on the spatiotemporal variable-step-size A* algorithm is proposed to address the problem of safe trajectory planning for incremental, wheeled, mobile robots in complex motion scenarios with multiple robots. After constructing the known conditions, the spatiotemporal variable-step-size A* algorithm is first used to perform a collision-avoiding initial spatiotemporal trajectory search, and a variable time step is utilized to ensure that the robot completes the search at the target speed. Subsequently, the trajectory is instantiated using B-spline curves in a numerical optimization considering constraints to generate the final smooth trajectory. The results of simulation tests in a field-shaped, complex, dynamic scenario show that the proposed trajectory planning method is more applicable, and the results indicate higher efficiency compared to the traditional method in the incremental robot trajectory planning problem.

Focusing on the characteristics of low-latency transmission and chain-distributed sensors along intelligent roads, this paper investigates a Multi-Chain Multi-Hop Routing Protocol (MMRP) suitable for roadside chain-distributed sensor networks. The aim is to address the low communication efficiency and poor system robustness of traditional chain-based networks. This study calculates the number of chains based on the communication capability of individual nodes and adopts TDMA to design a complete communication scheme, achieving efficient information transmission and self-organizing network routing. To address node failures, a self-check algorithm for control nodes is proposed for uploaded information, along with a rerouting mechanism where neighboring nodes of faulty points take over tasks, ensuring stable system operation. Simulation results show that MMRP achieves communication delay and packet loss rates of 0.72s and 2.1%, respectively, outperforming LEACH and PEGASIS protocols. The system energy consumption is 36.96J, comparable to traditional protocols. Moreover, under node failure conditions, MMRP maintains a low packet loss rate, acceptable communication delay, and energy consumption through rerouting, significantly improving system robustness and communication efficiency.