Search Results (46)

In this paper, a hierarchical adaptive control strategy is proposed to enhance the handling stability of distributed drive electric vehicles. In this strategy, the upper-level fuzzy controller calculates the additional yaw moment and rear wheel angle by utilizing the error between the actual and the target yaw velocity, as well as the error between the actual and the target sideslip angle. The quadratic programming algorithm is adopted to achieve the optimal torque distribution scheme through the lower-level controller, and the electronic stability control system (ESC) is utilized to generate the braking force required for each wheel. The four-wheel steering controller optimizes the rear wheel angle by using proportional feedforward combined with fuzzy feedback or Akerman steering based on the steering wheel angle and vehicle speed, through actuators such as active front-wheel steering (AFS) and active rear-wheel steering (ARS), which generate the steering angle of each wheel. This approach is validated through simulations under serpentine and double-lane-change conditions. Compared to uncontrolled and single-control strategies, the actuators are decoupled, the actual sideslip angle and yaw velocity of the vehicle can effectively track the target value, the actual response is highly consistent with the expected response, the goodness of fit exceeds 90%, peak-to-peak deviation with a small tracking error. Full article

To address the issue of insufficient trajectory tracking accuracy and the stability of distributed drive mining dump trucks under complex working conditions, this paper proposes a model predictive control (MPC) strategy based on genetic-particle swarm optimization (GAPSO). This strategy overcomes the limitations of traditional MPC controllers—where the weight matrix is fixed—by constructing a hierarchical optimization architecture that enables adaptive weight adjustment. An MPC-based trajectory tracking controller is developed using a three-degree-of-freedom vehicle dynamics model. Furthermore, to address the challenge of tuning MPC weight parameters, a GAPSO-based fusion optimization algorithm is introduced. This algorithm integrates the global search capability of genetic algorithms with the local convergence advantages of particle swarm optimization, enabling joint optimization of the state and control weight matrices. Simulation results demonstrate that under complex scenarios such as double lane change maneuvers, varying vehicle speeds, and different road adhesion coefficients, the proposed GAPSO-MPC controller significantly outperforms conventional MPC and PSO-MPC approaches in terms of lateral position tracking root mean square error. The method effectively enhances the robustness of trajectory tracking for distributed drive mining vehicles under disturbance conditions, offering a viable technical solution for high-precision control in autonomous mining systems. 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

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 challenges of enhancing driving stability and energy efficiency in distributed-drive electric vehicles, this paper proposes an extenics coordinated torque distribution control method that integrates energy efficiency optimization and vehicle stability. The primary contribution was the development of a vehicle stability assessment method grounded in extenics control theory, which was used to obtain the vehicle’s phase plane and stability region. Subsequently, an objective function with constraints for in-wheel motor torque distribution was formulated, targeting both optimal energy efficiency and maximum tire stability margin. Furthermore, the extension distances from the actual vehicle state to the stability boundaries were computed to determine adaptive weighting coefficients for these dual objectives. Finally, a Matlab/Simulink 2018a and Carsim2019 co-simulation platform was built to implement and test the proposed method. Simulations under the NEDC urban driving cycle and double-lane-change driving conditions were conducted to evaluate the following three distribution strategies: energy-optimal, stability-oriented, and extenics coordinated control. The results demonstrated that, regarding vehicle stability performance, extenics coordinated control showed a slightly inferior performance to the stability-oriented approach but substantially outperformed the energy-optimal strategy. In terms of energy consumption, the energy-optimal strategy achieved the lowest loss and the stability-oriented strategy showed the highest, while the extenics coordinated control presented intermediate results of 5.4 × 10⁹ J and 9.7 × 10⁷ J, respectively, under two driving conditions, representing reductions of 2.17% and 11.2% compared to the stability-oriented method. The proposed torque distribution method establishes an effective balance between energy-optimal and stability-oriented objectives. This method not only ensures satisfactory driving stability, but also reduces energy loss in in-wheel motors. 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 address the ‘mechanical’ return to original lane and similar non-humanized lane-changing issues that may occur in existing intelligent driving systems after completing overtaking maneuvers, this study proposes a humanized lane-changing decision method that incorporates driving style characteristics. First, based on the NGSIM dataset, we employ cluster analysis to systematically dissect human drivers’ lane-changing behavior patterns, laying the theoretical foundation for constructing a human-like decision framework. Second, a game model is established to precisely represent diverse driving styles by adjusting the weights of safety, efficiency, and comfort objectives. A reference line dynamic switching mechanism is then proposed to optimize lane-change paths by integrating vehicle speed and safety distance. Joint simulation results demonstrate superiority over dynamic programming (DP) methods in multiple aspects: under conservative driving mode, dual safety thresholds for following distance and speed significantly enhance safety and reliability. In general driving mode, driving stability and smoothness improved by 2.64% and 75.28%, respectively; in aggressive driving mode, lane-change speed increased by 7.06%. These improvements demonstrate that the human-like lane-changing strategy can autonomously achieve the optimal dynamic balance between safety, comfort, and efficiency tailored to different driving styles, providing an effective pathway for constructing high-performance autonomous driving decision systems. Full article

It is of great significance to construct a networked energy-saving driving strategy method and application framework to solve the problems of traffic disorder, speed fluctuations, and high energy consumption caused by frequent acceleration, deceleration, and lane changing of vehicles in road sections with variable traffic flow. Considering the mixed traffic scenario where autonomous vehicles and manually driven vehicles interact and infiltrate, a hybrid traffic flow vehicle energy-saving driving model was established, and the Dueling Double Deep Q-Network (D3QN) was used to optimize and solve the energy-saving driving model; Selecting Qingdao urban intersections as application scenarios, energy-saving driving strategy application facilities were constructed in simulation experiments to carry out simulation verification of energy-saving driving strategies for mixed traffic flow in the context of vehicle networking. The simulation results show that in different scenarios with different proportions of CAVs, the energy-saving strategy based on D3QN deep reinforcement learning algorithm can achieve fuel savings of 8.41%~6.67% compared to conventional strategies. Compared with the ordinary reinforcement learning algorithm Q-learning, its fuel saving rate is increased by 1.94%~1.5%, and the energy-saving effect becomes more significant with the increase of traffic density; From the perspective of dynamic characteristics, the speed stability under the control of D3QN algorithm is superior to Q-learning algorithm, and significantly better than conventional strategies, further highlighting the comprehensive advantages of D3QN algorithm in optimizing traffic flow status and energy consumption control. The energy-saving driving strategy in the networked environment can reduce fuel consumption caused by speed fluctuations and traffic flow frequency disturbances, and optimize the stability of traffic flow operation. 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

In order to address the significant nonlinear dynamic characteristics and limited trajectory tracking accuracy of unmanned vehicles under cornering conditions, this paper proposes a trajectory tracking control strategy based on Adaptive Model Predictive Control (AMPC). First, to enhance the accuracy of the vehicle model, an 11-degree-of-freedom vehicle dynamics model is established, incorporating pitch, roll, yaw, rotation around the Z-axis, and wheel-axis rotation. The vehicle motion equations are derived using Lagrangian analytical mechanics. Meanwhile, the tire model is optimized by accounting for the influence of vehicle attitude changes on tire mechanical properties. Based on the principles of nonlinear model predictive control (NMPC) and adaptive control, the AMPC algorithm is developed, its prediction model is constructed, and appropriate control constraints are defined to ensure improved accuracy and stability in trajectory tracking. Finally, simulations under double-lane-change and serpentine driving conditions are conducted using a co-simulation platform involving Carsim and Matlab/Simulink. The results demonstrate that the proposed controller achieves high trajectory tracking accuracy, effectively suppresses vehicle yaw, pitch, and roll motions, and enhances both the smoothness of trajectory tracking and the overall dynamic stability of the vehicle. Full article

Conflicts in freeway interchange diverging areas remain poorly understood, particularly their characteristics and severity determinants. To address this gap, we extracted over 20,000 vehicle trajectories from UAV footage at 16 interchange divergence zone across five multi-lane expressways using a YOLOX–DeepSORT method. From these trajectories, we identified longitudinal and lateral conflicts and classified their severity into minor, moderate, and severe levels using a two-dimensional extended time-to-collision metric. Subsequently, we incorporated 19 macroscopic traffic-flow and microscopic driver-behavior variables into four conflict-severity models–multivariate logistic regression, random forest, CatBoost, and XGBoost—and conducted to identify the key determinants of conflict severity based on the optimal models. The results indicate that lateral conflicts last longer and pose higher collision risks than longitudinal ones. Furthermore, moderate conflicts are most prevalent, whereas severe conflicts are concentrated within 300 m upstream of exit ramps. Specifically, for longitudinal conflicts, the most influential factors include speed difference, target-vehicle speed, truck involvement, traffic density, and exit behavior. In contrast, for lateral conflicts, the most critical factors include lane-change frequency, speed difference, target-vehicle speed, distance to the exit ramp, and truck proportion. Overall, these findings support the development of hazardous-driving warning systems and proactive safety management strategies in interchange diverging areas. Full article

In this paper, the concept of symmetry is utilized to design active fault tolerant trajectory-tracking control of autonomous distributed-drive electric vehicles—that is, the construction and the solution of active fault tolerant trajectory-tracking controllers are symmetrical. This paper presents a hierarchical fault tolerant control strategy for improving the trajectory-tracking performance of autonomous distributed-drive electric vehicles (ADDEVs) under steer-by-wire (SBW) system failures. Since ADDEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environments, the main control objective is to deal with the ADDEV trajectory-tracking control challenges of system uncertainties, SBW failures, and external disturbance. First, the differential steering dynamics model incorporating a 3-DOF coupled system and stability criteria based on the phase–plane method is established to characterize autonomous vehicle motion during SBW failures. Then, by integrating cascade active disturbance rejection control (ADRC) with Karush–Kuhn–Tucker (KKT)-based torque allocation, the active fault tolerant control framework of trajectory tracking and lateral stability challenges caused by SBW actuator malfunctions and steering lockup is addressed. The upper-layer ADRC employs an extended state observer (ESO) to estimate and compensate against uncertainties and disturbances, while the lower-layer utilizes KKT conditions to optimize four-wheel torque distribution to compensate for SBW failures. Simulations validate the effectiveness of the controller with serpentine and double-lane-change maneuvers in the co-simulation platform MATLAB/Simulink-Carsim^® (2019). Full article

With advances in underwater exploration and intelligent ocean technologies, wheeled underwater mobile robots are increasingly used for seabed surveying, engineering, and environmental monitoring. However, complex terrains centered on seabed soft slopes—characterized by wheel slippage due to soil deformability and force imbalance arising from slope variations—pose challenges to the accuracy and robustness of trajectory tracking control systems. Model predictive control (MPC), known for predictive optimization and constraint handling, is commonly used in such tasks. Yet, its performance relies on manually tuned parameters and lacks adaptability to dynamic changes. This study introduces a hybrid grey wolf-particle swarm optimization (GPSO) algorithm, combining the exploratory ability of a grey wolf optimizer with the rapid convergence of particle swarm optimization. The GPSO algorithm adaptively tunes MPC parameters online to improve control. A kinematic model of a four-wheeled differential-drive robot is developed, and an MPC controller using error-state linearization is implemented. GPSO integrates hierarchical leadership and chaotic disturbance strategies to enhance global search and local convergence. Simulation experiments on circular and double-lane-change trajectories show that GPSO-MPC outperforms standard MPC and PSO-MPC in tracking accuracy, heading stability, and control smoothness. The results confirm the improved adaptability and robustness of the proposed method, supporting its effectiveness in dynamic underwater environments. Full article

This study optimizes the deployment of autonomous taxis for safety at skewed intersections through a simulation-based approach, identifying an optimal penetration rate and control strategies. Here, we investigate the safety impacts of autonomous taxis (ATs) at such intersections using a simulation-based approach, leveraging the VISSIM traffic simulation tool and the Surrogate Safety Assessment Model (SSAM). Our study identifies an optimal AT penetration rate of approximately 0.5–0.7, as exceeding this range may lead to a decline in safety metrics such as TTC and PET. We find that connectivity among ATs does not linearly correlate with safety improvements, suggesting a nuanced approach to AT deployment is necessary. The “Normal” control strategy, which mimics human driving, shows a direct proportionality between AT penetration and TTC, indicating that not all levels of automation enhance safety. Our conflict analysis reveals distinct patterns for crossing, lane-change, and rear-end conflicts under various control strategies, underscoring the need for tailored approaches at skewed intersections. This research contributes to the discourse on AT safety and informs the development of traffic management strategies and policy frameworks that prioritize safety and efficiency in the context of skewed intersections. 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