Abstract
Rally drivers can perform extreme maneuvers and keep a vehicle on track by maximizing the vehicle agility. It is remarkable that this is achieved robustly, without a vehicle or tire model in mind. In this study, the Moment Method Diagram and Beta Method representations are used to show the maximum achievable yaw moment generated by the front and rear tires. A new maneuverability map is proposed to bypass the limitations imposed by the steady-state assumptions, based on the wheel slip - yaw moment representation. Furthermore, a simple driving automation strategy is developed to determine the sequence of inputs required for maximizing vehicle agility and negotiating extreme maneuvers. A finite state machine is designed and implemented using a two track vehicle model. The numerical results show that the finite state machine can resemble a rally driver.
| Original language | English |
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| Title of host publication | 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 978-1-5090-4240-1 |
| ISBN (Print) | 978-1-5090-4241-8 |
| DOIs | |
| Publication status | Published - 9 Feb 2017 |
| Event | 2016 IEEE Symposium Series on Computational Intelligence - Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016 |
Conference
| Conference | 2016 IEEE Symposium Series on Computational Intelligence |
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| Abbreviated title | SSCI 2016 |
| Country/Territory | Greece |
| City | Athens |
| Period | 6/12/16 → 9/12/16 |
Keywords
- agile maneuvering
- autonomous driving
- drift
- finite state machine
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems and Management
- Control and Optimization
- Artificial Intelligence