According to the results of a survey administered by the World Health Organization, the primary cause of the majority of traffic accidents is risky driving behavior. Consequently, the identification of risky driving behaviors is imperative for ensuring road safety. In order to effectively address the limitations of existing methods in modeling long-term dependencies, integrating external scenario information, and adapting to different driver personalities, this paper proposes a Transformer-based cross-attention feature fusion model for risky driving behavior recognition, referred to as CAFFT. This model utilizes a hierarchical Transformer encoder structure to capture fine-grained changes and long-term dependencies in driving behavior. Furthermore, the cross-attention mechanism, a cross-modal attention mechanism, is introduced to effectively fuse driving behavior features with weather-related external scenario features, thereby rendering the research scenario more aligned with real-world driving conditions. Finally, through cross-validation and cross-test set generalization experiments, the universality and stability of the method are verified. The experimental results on real driving datasets demonstrate that the CAFFT model achieves substantial performance enhancements in recognizing risky driving behavior. This improvement leads to enhanced accuracy and reliability in recognizing risky driving behavior, thereby providing substantial support for the development of intelligent driving assistance systems.

In the evolving landscape of mixed traffic environments, the interaction between Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs) introduces complex dynamics that challenge traditional traffic control paradigms. The Mixed Platoon Hierarchical Control (MPHC) model innovatively addresses these dynamics by integrating advanced control strategies that optimize the coexistence of CAVs and HVs. Specifically, the model introduces two novel approaches: the Incorporate Real-time Changes in Dynamic Headway (IRC_DH), which dynamically adjusts CAV headways by utilizing real-time traffic data and road conditions, enhancing platoon efficiency and reducing headway by up to 24.35%; and the Incorporate Controls to Improve Response Speed (IC_IRS) for HVs, a multi-variable control strategy that considers vehicle states, reducing speed differentials by as much as 53.125%, thereby stabilizing platoon dynamics and significantly lowering collision risks. These strategies distinguish themselves from existing approaches by providing a more precise, adaptable, and robust solution to the dynamic challenges of mixed traffic flow, addressing both efficiency and safety. Simulation results confirm that these strategies significantly improve traffic flow efficiency and safety, providing a scalable and adaptable framework for traffic management in mixed vehicular environments. This work makes a substantial contribution to the field by emphasizing a comprehensive, real-time, and multi-dimensional approach to managing mixed traffic environments, thereby advancing the state-of-the-art in CAV-HV integration.

This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output the pre-control parameters. Secondly, the back propagation neural network avoidance model, which combined with driver’s avoidance behavior, is developed for achieving the instantaneous collision avoidance control. Moreover, the optimal solution between control parameters and vehicle stability is obtained by using linear quadratic regulator. Finally, the accuracy of the avoidance model is verified by the semi-physical driver-in-the-loop simulation based on PreScan/Simulink. Results show that the automatic vehicle with the proposed avoidance model can accurately and effectively take pre-braking and micro-steering behavior. The proposed model can greatly reduce vehicle collision probability and effectively take both safety and comfort of collision avoidance into account. In addition, the robustness of the control model under different network penetration is discussed.

Cooperative adaptive cruise control (CACC) is a promising technology for vehicle platoons to increase roadway capacity. This paper proposes a networked Lagrange system (NLS) based CACC dynamic model based on which a hierarchical anti-disturbance controller is developed to solve the stability problem of CACC in vehicle platoons with time-varying parameter uncertainties, external disturbances, and directional dynamic communication topology. First, a hierarchical anti-disturbance controller for NLS is constructed which comprises three layers, these are, the adaptive smooth estimator-control layer, the distributed classifying amplitude-related layer, and the anti-disturbance control layer, in which the parameter uncertainties are estimated in the adaptive smooth estimator-control layer, the disturbance classification and distribute control are executed in the distributed classifying amplitude-related layer and the anti-disturbance control layer respectively. In addition, the proposed controller is extended to address the stability problem of CACC in vehicle platoons with time-varying parameter uncertainties, external disturbances, and directional dynamic communication topology conditions, which shows the versatility of the controller. Finally, comparison studies and simulation results are provided to demonstrate the effectiveness, significance, and advantages of the presented controllers.

Connected autonomous vehicle technology is conductive to promoting the transition from traditional merging control (e.g., ramp metering) to automated merging control. This paper proposes a platoon-based hierarchical merging control algorithm for on-ramp vehicles to achieve automated merging control under connected traffic environment. The proposed algorithm optimizes merging maneuvers of on-ramp vehicles to smooth their merging trajectories without frequent decelerations or stops at the end of the ramp, and to minimize disruption to the mainline traffic in the merging zone. A tactical layer controller is designed to select pre-target merging gaps for on-ramp vehicles, in which the future motion (i.e., acceleration and deceleration) of mainline vehicles is considered through the grey prediction model. An operational layer controller is constructed based on model predictive control to adjust the speed of on-ramp vehicles in advance, and controls on-ramp vehicles to merge into the pre-target merging gaps under state constraints (i.e., safe headway, maximum speed and so on). Through numerical simulation, the effectiveness of the proposed algorithm is validated under different merging scenarios. It is shown that on-ramp vehicles smoothly merge into the mainline within the pre-target merging gap at the same speed as adjacent mainline vehicles. Compared with the baseline merging control algorithm, the proposed algorithm significantly reduces both fuel consumption and travel time of on-ramp vehicles, and improves passenger comfort.