A hierarchical hybrid system of integrated longitudinal and lateral control for intelligent vehicles. (November 2020)
- Record Type:
- Journal Article
- Title:
- A hierarchical hybrid system of integrated longitudinal and lateral control for intelligent vehicles. (November 2020)
- Main Title:
- A hierarchical hybrid system of integrated longitudinal and lateral control for intelligent vehicles
- Authors:
- Chen, Keji
Pei, Xiaofei
Okuda, Hiroyuki
Zhu, Maolin
Guo, Xuexun
Guo, Konghui
Suzuki, Tatsuya - Abstract:
- Abstract: A hierarchical hybrid control system is proposed to cope with highly automated driving in highway environments with multiple lanes and surrounding vehicles. In the high-level layer, the discrete driving decisions are coordinated by the finite-state machine (FSM) based on the relative position identification and predictive longitudinal distance of the surrounding vehicles. The low-level layer is responsible for the vehicle motion control, where the model predictive control (MPC) approach is utilized to integrate the longitudinal and lateral control mainly including car-following control and lane changing control. The proposed control system focuses on two issues regarding safe driving on highways. On one hand, the subject vehicle must always keep a safe distance with its leading vehicle to avoid the rear-end collision. On the other hand, the subject vehicle should also overtake the preceding vehicle by safe lane changes if the desired speed is not achieved. The effectiveness of the hybrid control is tested in the simulation, whose results verify that the driving decisions are made reasonably and the vehicle motion control obeys stability and comfort requirements. Moreover, it is also indicated by the simulations in random scenarios that the control strategy is able to deal with most of ordinary situations on highways although some emergency situations or critical driving maneuvers of other vehicles are not considered. Highlights: The system focuses on theAbstract: A hierarchical hybrid control system is proposed to cope with highly automated driving in highway environments with multiple lanes and surrounding vehicles. In the high-level layer, the discrete driving decisions are coordinated by the finite-state machine (FSM) based on the relative position identification and predictive longitudinal distance of the surrounding vehicles. The low-level layer is responsible for the vehicle motion control, where the model predictive control (MPC) approach is utilized to integrate the longitudinal and lateral control mainly including car-following control and lane changing control. The proposed control system focuses on two issues regarding safe driving on highways. On one hand, the subject vehicle must always keep a safe distance with its leading vehicle to avoid the rear-end collision. On the other hand, the subject vehicle should also overtake the preceding vehicle by safe lane changes if the desired speed is not achieved. The effectiveness of the hybrid control is tested in the simulation, whose results verify that the driving decisions are made reasonably and the vehicle motion control obeys stability and comfort requirements. Moreover, it is also indicated by the simulations in random scenarios that the control strategy is able to deal with most of ordinary situations on highways although some emergency situations or critical driving maneuvers of other vehicles are not considered. Highlights: The system focuses on the environment with multiple lanes and surrounding vehicles. The driving decisions are coordinated in the finite state machine. The longitudinal and lateral maneuvers are controlled simultaneously … (more)
- Is Part Of:
- ISA transactions. Volume 106(2020)
- Journal:
- ISA transactions
- Issue:
- Volume 106(2020)
- Issue Display:
- Volume 106, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 106
- Issue:
- 2020
- Issue Sort Value:
- 2020-0106-2020-0000
- Page Start:
- 200
- Page End:
- 212
- Publication Date:
- 2020-11
- Subjects:
- Intelligent vehicle -- Automated driving -- Hybrid system -- Finite state machine -- Model predictive control
Engineering instruments -- Periodicals
Engineering instruments
Periodicals
Electronic journals
629.805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00190578 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.isatra.2020.07.009 ↗
- Languages:
- English
- ISSNs:
- 0019-0578
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4582.700000
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