Reinforcement learning (RL)–based car-following (CF) control strategies have attracted significant attention in academia, emerging as a prominent research topic in recent years. Most of these control strategies focus solely on the motion status of the immediately preceding vehicle. However, with the development of vehicle-to-vehicle (V2V) communication technologies, intelligent vehicles such as connected autonomous vehicles (CAVs) can gather information about surrounding vehicles. Therefore, this study proposes an RL-based CF control strategy that takes multivehicle scenarios into account. First, the trajectories of two preceding vehicles and one following vehicle relative to the subject vehicle (SV) are extracted from a highD dataset to construct the environment. Then the twin-delayed deep deterministic policy gradient (TD3) algorithm is implemented as the control strategy for the agent. Furthermore, a sequence-to-sequence (seq2seq) module is developed to predict the uncertain motion statuses of surrounding vehicles. Once integrated into the RL framework, this module enables the agent to account for dynamic changes in the traffic environment, enhancing its robustness. Finally, the performance of the CF control strategy is validated both in the highD dataset and in two traffic perturbation scenarios. In the highD dataset, the TD3-based prediction CF control strategy outperforms standard RL algorithms in terms of convergence speed and rewards. Its performance also surpasses that of human drivers in safety, efficiency, comfort, and fuel consumption. In traffic perturbation scenarios, the performance of the proposed CF control strategy is compared with the model predictive controller (MPC). The results show that the TD3-based prediction CF control strategy effectively mitigates undesired traffic waves caused by the perturbations from the head vehicle. Simultaneously, it maintains the desired traffic state and consistently ensures a stable and efficient traffic flow.

To systematically study the influence of a propensity for a particular driving style on car-following risk, a safety potential field risk-quantification method that considers driving style is proposed. First, we classify driving behaviors and construct a field-based safety potential car-following model via analogy with intermolecular interactions; second, we establish a risk-quantification model by considering driving style, risk exposure, and risk severity and classify the consequent risk into four levels, high risk, medium risk, low risk, and safe, using the fuzzy C-means algorithm. Finally, we predict the car-following risk using the LightGBM algorithm in real time. The experimental results show that the LightGBM algorithm can recognize up to 86% of medium–high risk levels compared to the Decision Tree and Random Forest Algorithms. It can achieve effective prediction of car-following risk, which provides sufficient warning information to drivers and helps improve the overall safety of vehicle operation.

To enhance the safety of lane changes for connected autonomous vehicles in an intelligent transportation environment, this study draws from potential field theory to analyze variations in the risks that vehicles face under different traffic conditions. The safe minimum vehicle distance is dynamically adjusted, and a comprehensive vehicle risk potential field model is developed. This model systematically quantifies the risks encountered by connected autonomous vehicles during the driving process, providing a more accurate assessment of safety conditions. Subsequently, vehicle motion is decoupled into lateral and longitudinal components within the Frenet coordinate system, with quintic polynomials employed to generate clusters of potential trajectories. To improve computational efficiency, trajectory evaluation metrics are developed based on vehicle dynamics, incorporating factors such as acceleration, jerk, and curvature. An initial filtering process is applied to these trajectories, yielding a refined set of candidates. These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. The algorithm is tested in a three-lane curved simulation environment that includes both constant-speed and variable-speed lane change scenarios. Results show that the collision risk between the target vehicle and surrounding vehicles remains below the minimum safety distance threshold throughout the lane change process, ensuring a high level of safety. Furthermore, across various driving conditions, the target vehicle’s acceleration, jerk, and trajectory curvature remained well within acceptable limits, demonstrating that the proposed lane change trajectory planning algorithm successfully balances safety, comfort, and smoothness, even in complex traffic environments.

Quantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural network and long short-term Memory (CNN-LSTM) deep-learning model, the future trajectory of the target vehicle is predicted. Risk is quantified by employing models that assess both the collision probability and collision severity, with deep-learning techniques applied for risk classification. Finally, the High-D dataset is used to predict the vehicle trajectory, from which the speed and acceleration of a target vehicle are derived to forecast driving risks. The results indicate that the CNN-LSTM model, when compared with standalone CNN and LSTM models, demonstrates a superior generalization performance, a higher sensitivity to risk changes, and an accuracy rate exceeding 86% for medium- and high-risk predictions. This improved accuracy and efficacy contribute to enhancing the overall safety of connected vehicle platoons.

To improve the safety of ramp vehicles changing lane and to shorten the merging distance, this paper explores the dynamic game interaction properties of vehicles merging and the consistency of vehicles’ decision-making behaviors at the macro-microscopic levels. Using the exiD dataset and evolutionary game theory, the merging behavior of ramp vehicles is modeled to explore the effects of different driving states on the evolutionary convergence of strategies. Based on the game cost theory, the lane choice behavior of mainline vehicles is modeled. Validated by SUMO software, the results show that the model in this paper can significantly improve the safety of vehicle merging and reduce the merging distance. The mainline vehicles are more inclined to change lane and cut out in advance when facing the ramp vehicles under the influence of the change of advantage in the subsequent game.

To characterize the dynamic interaction properties of heterogeneous traffic flow in the complex human–vehicle–road environment and to enhance the safety and efficiency of connected autonomous vehicles (CAVs), this study analyzes the self-driven particle characteristics and safety interaction behavior of CAVs based on molecular interaction potential. The molecular dynamics of potential interaction functions are employed to establish a dynamic quantization model for car-following (CF) safety potential, referred to as the molecular force field quantization model. To calibrate the model parameters, the Artificial Bee Colony Algorithm and the highD dataset are utilized, subsequently validating the reasonableness and effectiveness of the molecular dynamics model for vehicle tracking. The simulation results demonstrate that the proposed model can more accurately fit actual CF data, significantly improving vehicle travel safety and efficiency. Moreover, the profile of vehicle acceleration shows a lower mean absolute error and root mean square error compared to actual data, indicating that the model provides superior anti-interference fluctuation resistance and stability in CF scenarios. Overall, the proposed model effectively captures the microscopic CF behavior and vehicle–vehicle safety interactions, offering a theoretical foundation for further research into vehicle-following dynamics.