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.

Cooperative formation control of unmanned ground vehicles (UGVs) has become one of the important research hotspots in the application of UGV and attracted more and more attention in the military and civil fields. Compared with traditional formation control algorithms, reinforcement-learning-based algorithms can provide a new solution with a lower complexity for real-time formation control by equipping UGVs with artificial intelligence. Therefore, in this paper, a distributed deep-reinforcement-learning-based cooperative formation control algorithm is proposed to solve the navigation, maintenance, and obstacle avoidance tasks of UGV formations. More importantly, the hierarchical triangular formation structure and the newly designed Markov decision process for UGV formations of leader and follower attributes make the control strategy learned by the algorithm reusable, so that the formation can arbitrarily increase the number of UGVs and realize a more flexible expansion. The effectiveness and scalability of the algorithm is verified by formation simulation experiments of different scales.

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%.