Search Results (48)

Lane-changing and overtaking constitute a typical complex driving manoeuvre for intelligent vehicles operating on structured roads; this task demands that the vehicle not only plan a safe and smooth lane-change trajectory but also requires the control system to maintain high tracking accuracy and lateral stability. Addressing the challenges of real-time path planning and stable tracking control inherent in lane-changing and overtaking scenarios, this paper proposes a trajectory planning and control method that integrates an improved artificial potential field (APF) approach with a lateral–longitudinal cooperative controller. Regarding path planning, the proposed method constructs attractive and repulsive fields based on the APF framework, while introducing virtual target points, elliptical obstacle models, and velocity-dependent repulsive fields to mitigate the risk of local minima and enhance dynamic obstacle avoidance capabilities. To ensure trajectory continuity and trackability, a fifth-order polynomial is employed to smooth the planned path. Regarding control, the method utilises a Linear Quadratic Regulator (LQR)—optimised via a genetic algorithm—for lateral control; this is coupled with a dual-PID longitudinal controller that generates throttle and braking commands based on vehicle speed errors, thereby establishing a cooperative lateral–longitudinal tracking control strategy. The proposed method is validated using a CarSim–MATLAB/Simulink co-simulation platform. Simulation results demonstrate that the proposed method significantly improves trajectory-tracking accuracy and vehicle stability during lane-changing and overtaking manoeuvres. In a single lane-change scenario, the maximum lateral error is reduced from approximately 0.62 m to 0.22 m, and the heading angle error decreases from about 0.058 rad to 0.01 rad; in a continuous lane-changing scenario, the maximum lateral error drops from approximately 0.30 m to 0.04 m, while the heading angle error falls from about 0.016 rad to 0.005 rad. Furthermore, the yaw rate, sideslip angle, and lateral acceleration are reduced by 39.1%, 22.2%, and 28.9%, respectively. These results confirm that, under the specified simulation conditions, the proposed method exhibits superior tracking performance and stability. Future research could further explore more complex driving scenarios, such as curved roads, multi-vehicle interactions, sensor uncertainties, actuator delays, and real-vehicle field experiments. Full article

Existing research on cooperative control of connected and autonomous vehicles (CAVs) has primarily focused on structured freeway environments. Most existing approaches adopt lane-based modeling and discrete lane-change actions. These assumptions are unsuitable for toll plaza diverging areas without lane markings, where vehicles move toward multiple tollbooths. The absence of predefined lanes leads to continuous trajectory evolution, dense interactions, and increased safety risk. To address this limitation, this study proposes a multi-agent cooperative control framework based on Multi-Agent Proximal Policy Optimization (MAPPO) under a Centralized Training and Decentralized Execution (CTDE) architecture. The multi-agent formulation captures multi-vehicle interaction in toll plaza diverging areas, while centralized training improves learning stability. A target-path-oriented action space is introduced to replace the discrete lane-change action, enabling flexible tollbooth selection and continuous trajectory generation. The proposed cooperative strategy is trained and evaluated on a simulation platform structured under a Perception-Decision-Action framework, which provides a high-fidelity environment for weak-constraint traffic interactions. Simulation results based on real-world traffic data show that the proposed method improves traffic efficiency and enhances collision avoidance. Furthermore, comparative analyses are conducted to evaluate the model performance under varying traffic environments. Full article

The long-term coexistence of human-driven vehicles (HVs) and autonomous vehicles (AVs) in mixed traffic presents significant challenges for lane-change interactions on freeways. To address this, we propose a closed-loop decision-making framework, centered on Social Value Orientation (SVO), that covers the entire process from recognition to fallback execution. First, we use maximum-entropy inverse reinforcement learning (MaxEnt-IRL) to infer driver SVO parameters (${\theta }_{SVO}$) from the NGSIM dataset, quantifying the trade-off between selfish and cooperative behaviors as learnable weights. These parameters are then incorporated into a Transformer-based predictor via conditional embeddings, enabling the model to generate personalized trajectories from identical historical data. Furthermore, within a receding-horizon, game-theoretic framework, we combine preference-weighted payoffs with this conditional predictor and introduce a dynamic lane-change abort mechanism. This mechanism triggers a fallback maneuver, generated by an APF + MPC controller, if the expected return of continuing the lane change drops below that of aborting. Simulations across 1000 adversarial scenarios show that our method markedly improves the lane-change success rate and cruising efficiency compared to the IDM + MOBIL baseline. It also significantly reduces forced merges and hazardous events when encountering aggressive or selfish blocking vehicles, demonstrating the safety and robustness benefits of our preference-aware model and abort mechanism. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

To enhance the trajectory tracking accuracy and lateral stability of distributed-drive electric vehicles, a hierarchical cooperative control strategy optimized by the Genetic–Firefly Algorithm (G-FA) is proposed. First, bottom-level controllers for trajectory tracking utilizing a Linear Quadratic Regulator (LQR) and stability relying on Sliding Mode Control (SMC) are jointly optimized offline using the G-FA to address the limitations of empirical parameter tuning and effectively mitigate chattering. Compared to traditional Nonlinear Model Predictive Control (NMPC), which relies on computationally demanding dynamic programming, the proposed G-FA acts as an efficient approximate optimization method that significantly reduces the online computational burden while maintaining high control accuracy. Second, an adaptive cooperative mechanism based on desired yaw rate correction is introduced. By constructing two reference benchmarks—“tracking-oriented” and “stability-oriented”—a cooperative weighting coefficient adapts the fusion of control objectives based on the vehicle’s stability state. Hardware-in-the-loop (HIL) simulation results demonstrate that, under high-adhesion double lane change maneuvers, the proposed strategy reduces peak lateral error and sideslip angle by 31.53% and 28.08%, respectively, compared to traditional LQR. In low-adhesion S-curve limit maneuvers, where traditional LQR fails, the proposed strategy outperforms the NMPC benchmark, further reducing these indices by 61.98% and 8.33%, respectively, significantly improving control performance under extreme conditions. Full article

Achieving cooperative perception and decision-making among connected and autonomous vehicles (CAVs) in mixed-traffic ramp merge scenarios is crucial for building a swarm intelligence-based traffic control system. However, existing cooperative decision-making methods struggle to adequately model and represent the dynamic collaborative interactions among heterogeneous agents in mixed-traffic environments, which can lead to traffic congestion or even severe accidents in ramp merging areas. Therefore, this paper proposes an Enhanced Graph Reinforcement Learning algorithm based on a Vehicle Collaboration Graph (VCG-EGRL) to enable cooperative merging decisions for CAVs in mixed-traffic ramp merging scenarios. First, a vehicle collaboration intensity (VCI) model is designed to effectively model the intensity of collaborative interactions among vehicles. Then, based on the VCI model, the perception–communication relationships between vehicles and the vehicle-to-infrastructure (V2I) communication relationships are jointly constructed to form a local–global cooperative graph, which represents the dynamic collaborative relationships of the vehicle network from macro and micro perspectives and deeply explores the driving behavior of vehicles. Subsequently, a Graph Convolutional Network enhanced with Kolmogorov–Arnold Networks (KANs), referred to as GKAN, is employed to extract and aggregate the driving features of vehicles from the local–global graph. Finally, a graph mutual information maximization method is used to optimize the iterative process of the Graph Reinforcement Learning strategy, ensuring the generation of accurate lane-changing decisions for CAVs. Experimental results in ramp merging scenarios under varying traffic conditions demonstrate that the proposed method outperforms baseline models in terms of merging success rate, efficiency, and robustness. Full article

Emergency vehicles (EmVs) play a crucial role in providing prompt services to public rescue activities. However, due to the impacts of ordinary vehicles (OVs), EmVs are often blocked and cannot reach the rescue site in time. Therefore, a cooperative emergency vehicle priority driving scheme (CEVPDS) is proposed to ensure the EmVs’ travel efficiency. The proposed scheme is used for urban express roads under a vehicle-to-everything communications (V2X) environment and consists of two steps to ensure high-priority passage for the EmVs to reach the rescue accident site. The first step involves designing the EmV trajectory in advance. The second step requires the OVs to dynamically give way to the EmV by lane changing according to the EmV’s pre-planned trajectory established in the first step. Once the EmV trajectory is predefined, the relevant trajectory information is transmitted to surrounding OVs via V2X communication. OVs ahead of the EmV are then scheduled to provide adequate road space by lane changing, while the OVs behind it are prohibited from overtaking it. We conducted simulations using the SUMO platform. Compared with the Fixed-Lane Strategy (FLS), the proposed scheme achieves multiple improvements in multiple aspects: it drastically shortens the EmVs response time, significantly mitigates the impacts on OVs, and fewer lane changes for both EmVs and OVs. As a result, the scheme not only enhances the travel efficiency of EmVs but also guarantees the safety, symmetry, and efficiency of the overall urban traffic system. Full article

Mixed traffic flow composed of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) represents a core characteristic of intelligent transportation systems. However, its operational efficiency is significantly constrained by lane management strategies and CAV cooperative driving behaviors. To investigate this, a cellular automata-based simulation model is developed that integrates multiple car-following rules, a lane-changing strategy, and a platoon coordination mechanism. Through a systematic comparison of 13 lane management strategies in one-way two-lane and three-lane configurations, this study analyzes the influence mechanisms of lane allocation and cooperative driving on traffic flow, considering fundamental diagram characteristics, operating speed, CAV degradation behavior, and maximum platoon size. The results indicate that the performance of different strategies exhibits phased evolution with increasing CAV penetration rates. At low penetration rates, providing relatively independent space for HDVs effectively suppresses random disturbances and improves throughput. At medium to high penetration rates, dedicated CAV lanes—especially those with spatial continuity—enable cooperative platoons to fully leverage their advantages, leading to significant improvements in traffic capacity and operational stability. These findings demonstrate an optimal alignment between cooperative driving mechanisms and lane configurations, offering theoretical support for highway lane management in mixed traffic environments. Full article

Autonomous lane-changing is one of the most critical and complex tasks in automated driving. Recent progress in reinforcement learning (RL) has shown strong potential to help autonomous vehicles (AVs) make safe and flexible lane-change decisions in real time under uncertain traffic conditions. In the current studies, there is a lack of a common structure that links RL algorithms, simulation tools, and performance evaluation methods. This paper presents a detailed examination of RL-based lane-changing systems in AVs, tracing their development from early rule-based models to modern learning-based approaches. It introduces a clear classification of lane-changing types—discretionary, mandatory, cooperative, and emergency—and connects each to the most suitable RL methods, including value-based, policy-based, actor–critic, model-based, and hybrid algorithms. Each method is examined for its performance, safety, and computational demands. Furthermore, it reviews major simulation environments, such as SUMO, CARLA, and SMARTS, and summarizes key evaluation measures related to safety, efficiency, comfort, and real-time performance. The comparison shows open research challenges, including model adaptation, safety assurance, and transfer from simulation to real-world driving. Finally, it outlines promising directions for future work, such as cooperative decision-making, safe and explainable RL, and lightweight models for real-time use. This review provides a clear foundation and practical guide for developing reliable and understandable RL-based lane-changing systems for future intelligent transportation. Full article

In the centralized scheduling mode of automated container terminals, Automated Guided Vehicles (AGVs) often experience decision-making delays caused by system information-processing bottlenecks, which significantly affect path-planning efficiency and are particularly evident in sudden-traffic scenarios. To address this issue, this paper incorporates the artificial potential field (APF) into the cost function of Model Predictive Control (MPC) and develops a dual-trigger mechanism for lane-change and lane-return MPC obstacle-avoidance framework (Event-Triggered Model Predictive Control, EMPC). This framework integrates an obstacle-triggered local optimization mechanism and a lane-change trigger, enabling AGV to perform autonomous and dynamically responsive local obstacle avoidance, thereby improving local path-planning efficiency. Furthermore, a Proximal Policy Optimization (PPO)-based strategy is introduced to adaptively adjust the obstacle-weighting parameters within the EMPC cost function, enhancing both obstacle-avoidance and lane-keeping performance. Under multi-lane overtaking conditions, a lane-change trigger—implemented as a dual-phase “lane-change–return” mechanism—is employed, in which lateral optimization is activated only during critical phases, reducing online computational load by at least 28% compared with conventional MPC strategies. The experimental results demonstrate that the proposed PPO–EMPC architecture exhibits high robustness, real-time performance, and scalability under dynamic and partially observable environments, providing a practical and generalizable decision-making paradigm for cooperative AGV operations in automated container terminals. Full article

To address the difficulty of balancing trajectory-tracking accuracy and yaw stability for distributed-drive four-axle heavy trucks under high-speed and large-curvature cornering conditions, this paper proposes a hierarchical cooperative control strategy. The upper layer employs Sliding Mode Control (SMC) to achieve precise trajectory tracking, while the lower layer integrates a sliding-mode-based Direct Yaw Moment Control (DYC) and a differential braking allocation strategy to enhance vehicle stability. TruckSim–Simulink co-simulation results demonstrate that, under large-curvature scenarios such as S-shaped paths, sharp lane changes, and single-lane transitions, the proposed strategy outperforms the conventional SMC method. Specifically, the maximum lateral deviation is reduced by 19.23–23.02%, the peak heading angle error decreases from 5.3° to 3.5°, the maximum yaw rate drops from 12.6°/s to 4.6°/s (a 63.49% reduction), and the peak sideslip angle at the vehicle’s center of mass converges from 4.6° to 3.8° (a 17.39% decrease). The results indicate that the proposed strategy achieves coordinated optimization of trajectory tracking and yaw stability under high-speed, large-curvature cornering conditions, providing both theoretical support and engineering value for high-dynamic control of distributed-drive heavy trucks. Full article

To address the poor stability of four-wheel steering vehicles under extreme conditions, this paper proposes a coordinated control strategy for vehicles with four-wheel independent drive. The strategy combines the Active Four-Wheel Steering system with the Direct Yaw Moment Control system. First, a shared steering control model is constructed by considering both the vehicle’s path-tracking performance and handling stability. Based on this model, a control strategy for the four-wheel steering system is proposed using a non-cooperative Nash game. Next, a direct yaw moment controller is designed to improve vehicle lateral stability under dangerous driving conditions. To achieve synergy between rear-wheel steering and direct yaw moment control, a rule-based coordination strategy is introduced to optimize the working intervals of each sub-controller. Finally, experimental verification is performed under double-lane-change and slalom conditions using the CarSim/Simulink hardware-in-the-loop platform. All computations were done in MATLAB R2024a, using specific m-files and Simulink functions for implementation, and the controller was implemented using the Micro-Autobox tool. The results demonstrate that the proposed control strategy significantly enhances vehicle path-tracking accuracy and handling stability under extreme driving conditions. Full article

To resolve the inherent coupling conflict between trajectory tracking and lateral stability in distributed electric drive buses, this paper proposes a hierarchical cooperative control framework. A simplified two-degree-of-freedom (2-DOF) vehicle model is first established, and kinematically derived reference states for stable motion are computed. At the upper level, a model predictive controller (MPC) generates real-time steering commands while explicitly minimizing lateral tracking error. At the lower level, a proportional integral derivative (PID)-based roll moment controller and a linear quadratic regulator (LQR)-based direct yaw moment controller are designed, with four-wheel torque distribution achieved via quadratic programming subject to friction circle and vertical load constraints. Co-simulation results using TruckSim and MATLAB/Simulink demonstrate that, during high-speed single-lane-change maneuvers, peak lateral error is reduced by 11.59–18.09%, and root-mean-square (RMS) error by 8.67–14.77%. Under medium-speed double-lane-change conditions, corresponding reductions of 3.85–12.16% and 4.48–11.33% are achieved, respectively. These results fully validate the effectiveness of the proposed strategy. Compared with the existing MPC–direct yaw moment control (DYC) decoupled control framework, the coordinated control strategy proposed in this paper achieves the optimal trade-off between trajectory tracking and lateral stability while maintaining the quadratic programming solution delay below 0.5 milliseconds. Full article

Collision avoidance between vehicles is a great challenge, especially in the context of mixed driving of connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs). Advances in automation and connectivity technologies provide opportunities for CAVs to drive cooperatively. This paper proposes a proactive collision avoidance model, aiming to avoid collisions by controlling the speed and lane-changing behavior of CAVs. In the model, the subject vehicle first collects information about surrounding lanes and judges the traffic conditions; it then chooses to decelerate or change lanes to avoid collisions. The subject vehicle also searches for the optimal vehicle in the surrounding lanes for cooperation. The effectiveness of the proposed collision avoidance model is verified through the Python-SUMO platform. The experimental results show that the performance of the collision avoidance model is better than that of the cooperative adaptive cruise control (CACC) model in terms of average speed, lost time and the number of vehicle conflicts, proving the advantages of the proposed model in safety and efficiency. Full article

With the advancement of autonomous driving technology, transportation systems are inevitably confronted with mixed traffic flows consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Current research has predominantly focused on implementing homogeneous control strategies for ramp merging vehicles in such scenarios, which, however, may result in the oversight of specific requirements in fine-grained traffic scenarios. Therefore, a classified cooperative merging strategy is proposed to address the challenges of microscopic decision-making in hybrid traffic environments where HDVs and CAVs coexist. The optimal cooperating vehicle on the mainline is first selected for the target ramp vehicle based on the principle of minimizing time differences. Three merging strategies—joint coordinated control, partial cooperation, and speed limit optimization—are then established according to the pairing type between the cooperating and ramp vehicles. Optimal deceleration and lane-changing decisions are implemented using the average speed change rate within the control area to achieve cooperative merging. Validation via a SUMO-based simulation platform demonstrates that the proposed strategy reduces fuel consumption by 6.32%, NO_x emissions by 9.42%, CO₂ emissions by 9.37%, and total delay by 32.15% compared to uncontrolled merging. These results confirm the effectiveness of the proposed strategy in mitigating energy consumption, emissions, and vehicle delays. Full article