To explore the potential relationship between the leading vehicle and the following vehicle during car-following, we proposed a novel car-following model combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network. Firstly, 400 car-following periods were extracted from the natural driving database and the OpenACC car-following experiment database. Then, we developed a CNN-LSTM car-following model, and the CNN is employed to analyze the potential relationship between the vehicle’s dynamic parameters and to extract the features of car-following behavior to generate the feature vector. The LSTM network is adopted to save the feature vector and predict the speed of the following vehicle. Finally, the CNN-LSTM model is trained and tested with the extracted car-following trajectories data and compared with the classical car-following models (LSTM model, intelligent driver model). The results show that the accuracy and the ability to learn the heterogeneity of the proposed model are better than the other two. Furthermore, the CNN-LSTM model can accurately reproduce the hysteresis phenomenon of congested traffic flow and apply to heterogeneous traffic flow mixed with adaptive cruise control vehicles on the freeway, which indicates that it has strong generalization ability.

The lateral control of the vehicle is significant for reducing the rollover risk of high-speed cars and improving the stability of the following vehicle. However, the existing car-following (CF) models rarely consider lateral control. Therefore, a CF model with combined longitudinal and lateral control is constructed based on the three degrees of freedom vehicle dynamics model and reinforcement learning method. First, 100 CF segments were selected from the OpenACC database, including 50 straight and 50 curved road trajectories. Afterward, the deep deterministic policy gradient (DDPG) car-following model and multi-agent deep deterministic policy gradient (MADDPG) car-following model were constructed based on the deterministic policy gradient theory. Finally, the models are trained with the extracted trajectory data and verified by comparison with the observed data. The results indicate that the vehicle under the control of the MADDPG model and the vehicle under the control of the DDPG model are both safer and more comfortable than the human-driven vehicle (HDV) on straight roads and curved roads. Under the premise of safety, the vehicle under the control of the MADDPG model has the highest road traffic flow efficiency. The maximum lateral offset of the vehicle under the control of the MADDPG model and the vehicle under the control of the DDPG model in straight road conditions is respectively reduced by 80.86% and 71.92%, compared with the HDV, and the maximum lateral offset in the curved road conditions is lessened by 83.67% and 78.95%. The proposed car following model can provide a reference for developing an adaptive cruise control system considering lateral stability.

Migration of fine materials such as soil from the roadbed and the ground will gradually pollute the aggregate in the arrester bed of truck escape ramps. However, there are few studies on the impact of aggregate pollution of the arrester bed on the stopping distance of runaway vehicles. This paper uses the discrete element method to study the influence of aggregates with different degrees of pollution on stopping distance by taking silty cohesive soil as a typical pollutant. In this paper, the stopping process of the uncontrolled vehicle on the arrester bed with different pollution levels was numerically simulated. The simulation results show that the uncontrolled vehicle’s stopping distance increases with the contaminated aggregate’s soil content. The simulation results show that when the soil content in the contaminated aggregate is less than 15%, the increase in the stopping distance of the uncontrol vehicle is less than 5%; when the soil content is 20–25%, the stopping distance of the uncontrolled vehicle increases by more than 20%; and when the soil content is 30–35%, the stopping distance of uncontrol vehicle increases by more than 50%. Different maintenance measures should be taken according to the increase in stopping distance.