Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

Jui-An Yang,Chung-Hsien Kuo

doi:10.3390/electronics10212703

Jui-An Yang, Chung-Hsien Kuo

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https://doi.org/10.3390/electronics10212703

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This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

Highlights

Vehicle positioning and path tracking are essential to the development of autonomous vehicles
The overall system is primarily composed of a unscented Kalman filter (UKF)-based position estimator and an reinforcement learning (RL)-based model predictive control (MPC) (RLMPC) controller design
We evaluated if the error percentage produced by the UKF was superior with state-of-the-art methods

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Vehicle positioning and path tracking are essential to the development of autonomous vehicles. Vehicle positioning is responsible for providing stable, real-time, and accurate location information for navigations. The path tracking is developed to navigate the vehicle by following the desired trajectory to reduce the tracking errors. The position accuracy with a conventional, stand-alone global positioning system (GPS) is approximately. By applying real-time kinematic (RTK) technology, the GPS positioning accuracy can be improved to the centimeter level. The RTK-GPS performance is still restricted when jumping and drifting occur in an obstructed area, such as a blockage and multipath effects in an urban region with buildings and landscapes, under a bridge, or in a tunnel

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