The decision-making models that are able to deal with complex and dynamic urban intersections are critical for the development of autonomous vehicles. A key challenge in operating autonomous vehicles robustly is to accurately detect the trajectories of other participants and to consider safety and efficiency concurrently into interactions between vehicles. In this work, we propose an approach for developing a tactical decision-making model for vehicles which is capable of predicting the trajectories of incoming vehicles and employs the conflict resolution theory to model vehicle interactions. The proposed algorithm can help autonomous vehicles cross intersections safely. Firstly, Gaussian process regression models were trained with the data collected at intersections using subgrade sensors and a retrofit autonomous vehicle to predict the trajectories of incoming vehicles. Then, we proposed a multiobjective optimization problem (MOP) decision-making model based on efficient conflict resolution theory at intersections. After that, a nondominated sorting genetic algorithm (NSGA-II) and deep deterministic policy gradient (DDPG) are employed to select the optimal motions in comparison with each other. Finally, a simulation and verification platform was built based on Matlab/Simulink and PreScan. The reliability and effectiveness of the tactical decision-making model was verified by simulations. The results indicate that DDPG is more reliable and effective than NSGA-II to solve the MOP model, which provides a theoretical basis for the in-depth study of decision-making in a complex and uncertain intersection environment.

Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

This replication package includes the complete code used in our study titled “3D LiDAR and image data-level fusion for traffic vehicle detection" The materials provided are essential for researchers and practitioners interested in replicating our experiments and validating the findings.