Research on Human Driving Characterised Trajectory Planning and Trajectory Tracking Control Based on a Test Track

Xu, Xing; Jiang, Xinwei; Xie, Ju; Wang, Rui; Li, Minglei

doi:10.1007/s12555-021-0785-6

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The major trajectory tracking methods that are presently employed include pure tracking algorithms founded on geometric modelling, Stanley's algorithm, PID control algorithms based on kinematics and dynamics models, sliding mode control, and model predictive control algorithms [29][30][31]. Pacheco et al [32] described the online performance of local path tracking control laws for mobile robots, comparing the use of PID controllers and MPC.…”

Section: Lqr Controller-based Trajectory Trackingmentioning

confidence: 99%

Design of a Collaborative Vehicle Formation Control Simulation Test System

Xu,

Zhang,

Ding

et al. 2023

Electronics

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The purpose of this research is to tackle one of the most difficult issues in the realm of self-driving cars, which is the testing of advanced self-driving application scenarios. Thus, this study proposes a simulation testing system based on hardware-in-the-loop simulation technology. This system can enable data exchange between hardware systems as well as replicate and evaluate the algorithmic operations of the equipment under laboratory conditions. The system can integrate scenario simulation software with MATLAB to evaluate algorithm performance. The vehicle formation control system is tailored for collaborative vehicle formation management scenarios and tested in the simulation test system. The findings display the functional integrity of the vehicle formation control system, the reliability of lane changing and the stability and safety of cruising. It additionally demonstrates that the simulation testing system has the ability to recreate cooperative vehicle arrangement management situations and assess their functionality and performance. In forthcoming research, comprehensive functional and performance assessments will be executed on various typical scenarios for advanced autonomous driving applications in order to authenticate the simulation test system’s applicability.

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Section: Lqr Controller-based Trajectory Trackingmentioning

confidence: 99%

Design of a Collaborative Vehicle Formation Control Simulation Test System

Xu,

Zhang,

Ding

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…The slow response and trajectory deviation caused by time delays may prevent vehicles from making timely and correct judgments and executing corresponding actions in emergency situations, thereby increasing the risk of accidents. Therefore, in intelligent vehicle trajectory tracking control, effective handling of time delay issues is an important link for ensuring effectiveness in vehicle trajectory tracking control and driving safety [14][15][16]. By optimizing the processing speed of sensor data, constructing predictive control algorithms with high solving ability, introducing robust control strategies, adjusting control parameters in real time, and establishing accurate models, the impact of time delay factors on control systems can be effectively mitigated, and the accuracy and stability of trajectory tracking can be improved.…”

Section: Introductionmentioning

confidence: 99%

Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control

Jin,

Li,

Chen

2024

Symmetry

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The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the motion control of unmanned ground vehicles, a vehicle coordination control method based on chaotic particle swarm optimization (CPSO) and model predictive control (MPC) algorithms is proposed. To achieve coordinated control of vehicle trajectory tracking and yaw stability, a model predictive controller was designed with the objective of minimizing trajectory tracking errors and yaw stability tracking errors. The required front wheel angle and yaw torque control variables were obtained by solving nonlinear constraint optimization. At the same time, considering the problems of low computational efficiency, high solving time, and local optimization in model predictive control, a chaotic particle swarm optimization algorithm is introduced to solve the optimization constraint problem within model predictive control, thereby effectively improving the computational efficiency and accuracy of the model predictive trajectory tracking controller. The results show that compared with MPC, the multi-objective function optimization solution time and vehicle lane changing time of CPSOMPC improved by 24.51% and 7.21%, respectively, which indicates the coordinated control method that combines the CPSO and MPC algorithms can effectively improve trajectory tracking performance while ensuring vehicle lateral stability.

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Trajectory planning and tracking of dynamic lane change for autonomous buses considering vehicle stability in dynamic traffic scenarios

Nie,

Zhou,

Lian

2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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Trajectory planning and tracking of lane change are critical technologies for autonomous buses. Characteristics of the buses susceptible to stability problems resulting from the high height, large passenger capacity and long lengths, coupling the dynamic traffic with the dynamic changes in the states of adjacent vehicles and road adhesion coefficient, put forward great challenges in lane change for autonomous buses (ABs). To cope with the foregoing challenges, a framework is proposed to achieve the trajectory planning and tracking of dynamic lane change for ABs. For trajectory planning approach, the trajectory planning and replanning is optimized in the safe range of longitudinal length of the lane-changing trajectory to obtain the real-time reference trajectory, combining consideration of vehicle yaw, roll stability and lane-changing efficiency. The minimum longitudinal length of lane-changing trajectory determined by the yaw stability and roll stability of ABs, combined with the maximum length formed by the adjacent vehicles with dynamic states, form the real-time safe range for lane-changing trajectory planning. For trajectory tracking approach, a tracking approach using model predictive control based on multipoint preview is proposed to achieve the real-time planned trajectory tracking considering buses stability. The effectiveness of the proposed strategy is evaluated by simulating an experimentally validated Trucksim model in different dynamic traffic scenarios to demonstrate the capability of the strategy in trajectory planning and tracking, and guaranteeing vehicle stability for dynamic lane change of ABs.

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Research on Human Driving Characterised Trajectory Planning and Trajectory Tracking Control Based on a Test Track

Cited by 3 publications

References 28 publications

Design of a Collaborative Vehicle Formation Control Simulation Test System

Design of a Collaborative Vehicle Formation Control Simulation Test System

Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control

Trajectory planning and tracking of dynamic lane change for autonomous buses considering vehicle stability in dynamic traffic scenarios

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