Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles

Li, Xiaohui; Sun, Zhenping; Cao, Dongpu; Liu, Daxue; He, Hangen

doi:10.1016/j.ymssp.2015.10.021

Cited by 236 publications

(105 citation statements)

References 35 publications

Supporting

Mentioning

105

Contrasting

Order By: Relevance

“…As showing in Fig.3, we demonstrate a comparison of our methods and model predictive local path generation method proposed by Li et al [14]. The path generation method based on model predictive, because of the strong nonlinearity of the system model, can't be solved by analytical method, so it needs to be solved by numerical integration method.…”

Section: B Piecewise Spiral Curve Path Generation Methodsmentioning

confidence: 95%

“…The overall idea of state space sampling is that the end of the trajectory is first sampled and then the trajectory generation problem is transformed into a Two-Point Boundary Value Problem [14]. In this part, we propose a sampling-based piecewise spiral curve path generation method .The generation method mainly includes three steps.…”

Section: Sampling-based Piecewise Spiral Curve Path Generation Mmentioning

confidence: 99%

See 1 more Smart Citation

Trajectory Planning for Autonomous Ground Vehicles Driving in Structured Environments

Li³

et al. 2017

2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

Self Cite

View full text Add to dashboard Cite

Abstract-To solve the problem of trajectory planning for Autonomous Ground Vehicles (AGVs) in structured environments, a kinematically-feasible trajectory planning approach based on road models is proposed. In order to cope with obstacle avoidance on roads, we develop an efficient path generation method, using the piecewise spiral curve to generate a set of continuous curvature paths which satisfy the constraints of the start and end point boundaries. Based on the proposed optimization function, the optimal path is selected. Compared with the model-based predictive trajectory planner, the method proposed in this paper can effectively avoid the problem of slow convergence or no feasible solution. The experimental results show that: The proposed method not only retains most of the advantages of model-based predictive trajectory planning, but also reduces the computational complexity to meet the realtime requirements. The curvature of the generated path is continuous and is suitable for the actual control of the vehicle. Simulation results show that the proposed method can track the reference path smoothly and avoid static obstacles successfully.

show abstract

Section: B Piecewise Spiral Curve Path Generation Methodsmentioning

confidence: 95%

Section: Sampling-based Piecewise Spiral Curve Path Generation Mmentioning

confidence: 99%

Trajectory Planning for Autonomous Ground Vehicles Driving in Structured Environments

Li³

et al. 2017

2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The parameters of the vehicle and path are listed in Table 1. The path for the simulated steering controller to follow is a 584 m circuit generated by the pathgeneration method of Reference [25], as shown in Figure 9a. The desired path is parameterized as a curvature profile that varies with distance counterclockwise along the path, which is shown in Figure 9b.…”

Section: Controller Performancementioning

confidence: 99%

Design of a Path-Tracking Steering Controller for Autonomous Vehicles

Sun

Xin

et al. 2018

Energies

View full text Add to dashboard Cite

This paper presents a linearization method for the vehicle and tire models under the model predictive control (MPC) scheme, and proposes a linear model-based MPC path-tracking steering controller for autonomous vehicles. The steering controller is designed to minimize lateral path-tracking deviation at high speeds. The vehicle model is linearized by a sequence of supposed steering angles, which are obtained by assuming the vehicle can reach the desired path at the end of the MPC prediction horizon and stay in a steady-state condition. The lateral force of the front tire is directly used as the control input of the model, and the rear tire's lateral force is linearized by an equivalent cornering stiffness. The course-direction deviation, which is the angle between the velocity vector and the path heading, is chosen as a control reference state. The linearization model is validated through the simulation, and the results show high prediction accuracy even in regions of large steering angle. This steering controller is tested through simulations on the CarSim-Simulink platform (R2013b, MathWorks, Natick, MA, USA), showing the improved performance of the present controller at high speeds.

show abstract

“…In reality, both government and academia have reached an agreement that the autonomous vehicle needs to conduct comprehensive, systematic, and rigorous tests before it is officially commercialized. However, the testing of autonomous vehicle requires extremely time-consuming work and huge economic cost [10]. An autonomous vehicle consists of five functions (as shown in Figure 1): (1) sensing and location systems; (2) global route planning; (3) behavior reasoning; (4) trajectory planning; (5) trajectory tracking control.…”

Section: Introductionmentioning

confidence: 99%

“…However, the trajectory tracking control process needs to be tested with real vehicles as the vehicle kinematic and dynamic constraints; controller responsiveness will influence the reliability, smooth, and comfort of autonomous vehicles. For the trajectory tracking control function, it requires that the control algorithm can provide the spatial path planning and handle the direction/speed of the vehicle according to the real-time surrounding traffic information and high-accuracy map information [10].…”

Section: Introductionmentioning

confidence: 99%

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

Wang

Zhang³

et al. 2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

With the advent of autonomous vehicles, in particular its adaptability to harsh conditions, the research and development of autonomous vehicles attract significant attention by not only academia but also practitioners. Due to the high risk, high cost, and difficulty to test autonomous vehicles under harsh conditions, the hardware-in-the-loop (HIL) scaled platform has been proposed as it is a safe, inexpensive, and effective test method. This platform system consists of scaled autonomous vehicle, scaled roadway, monitoring center, transmission device, positioning device, and computers. This paper uses a case of the development process of tracking control for high-speed U-turn to build the tracking control function. Further, a simplified vehicle dynamics model and a trajectory tracking algorithm have been considered to build the simulation test. The experiment results demonstrate the effectiveness of the HIL scaled platform.

show abstract

Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles

Cited by 236 publications

References 35 publications

Trajectory Planning for Autonomous Ground Vehicles Driving in Structured Environments

Trajectory Planning for Autonomous Ground Vehicles Driving in Structured Environments

Design of a Path-Tracking Steering Controller for Autonomous Vehicles

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

Contact Info

Product

Resources

About