Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator |
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| Affiliation: | 1. School of Mechanical and Aerospace Engineering, Seoul National University, 599 Gwanangno, Gwanak-Gu, Seoul 151-742, Republic of Korea;2. Hyundai R&D Center, 772-1, Jangduk-Dong, Hwaseong-si, Gyeonggi-Do 445-706, Republic of Korea;1. State Key Laboratory of Industrial Control Technology, Zhejiang University, 310027 Hangzhou, China;2. Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li 320, Taiwan, ROC;3. ABB Corporate Research Center Germany, 68526 Ladenburg, Germany;1. Department of Engineering Mechanics, State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China;2. Department of Engineering Aerospace, State Key Laboratory of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;1. Division of Urology, Department of Surgery, the Ottawa Hospital and University of Ottawa, Ottawa, Ontario, Canada;2. Ottawa Hospital Research Institute, Ottawa, Ontario, Canada;3. University Health Network, Toronto, Ontario, Canada;4. University of Western Ontario, London, Ontario, Canada;5. QEII Health Sciences Centre, Halifax, Nova Scotia, Canada;6. McGill University, Montreal, Quebec, and University of Alberta, Edmonton, Alberta, Canada;1. DII, Dipartimento di Ingegneria Industriale, University of Trento, Trento, Italy;2. DISI, Dipartimento di Ingegneria e Scienza dell’Informazione, University of Trento, Trento, Italy |
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| Abstract: | This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving. |
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| Keywords: | Automated driving control Model predictive control Vehicle traffic simulator Real-time implementation Motion planning |
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