A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios
This paper describes the design of unified active safety framework that combines trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles into a single constrained-optimal-control-based system. This framework allows for multiple actuation modes, diverse trajectory-pl...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/58809 |
| _version_ | 1826200844410814464 |
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| author | Iagnemma, Karl Peters, Steven Conrad Anderson, Sterling J. Pilutti, Tom E. |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Iagnemma, Karl Peters, Steven Conrad Anderson, Sterling J. Pilutti, Tom E. |
| author_sort | Iagnemma, Karl |
| collection | MIT |
| description | This paper describes the design of unified active safety framework that combines trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles into a single constrained-optimal-control-based system. This framework allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. The vehicle navigation problem is formulated as a constrained optimal control problem with constraints bounding a navigable region of the road surface. A model predictive controller iteratively plans the best-case vehicle trajectory through this constrained corridor. The framework then uses this trajectory to assess the threat posed to the vehicle and intervenes in proportion to this threat. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. Simulated results are presented here to demonstrate the framework's ability to incorporate multiple threat thresholds and configurable intervention laws while sharing control with a human driver. |
| first_indexed | 2024-09-23T11:42:19Z |
| format | Article |
| id | mit-1721.1/58809 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:42:19Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/588092022-09-27T21:21:21Z A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios Iagnemma, Karl Peters, Steven Conrad Anderson, Sterling J. Pilutti, Tom E. Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Laboratory for Manufacturing and Productivity Iagnemma, Karl D. Iagnemma, Karl Peters, Steven Conrad Anderson, Sterling J. MPC Semi-autonomous control Active safety Hazard avoidance Human-machine interaction Lane keeping Mobile robotics Model predictive control Shared adaptive control Threat assessment Vehicle autonomy Vehicle safety This paper describes the design of unified active safety framework that combines trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles into a single constrained-optimal-control-based system. This framework allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. The vehicle navigation problem is formulated as a constrained optimal control problem with constraints bounding a navigable region of the road surface. A model predictive controller iteratively plans the best-case vehicle trajectory through this constrained corridor. The framework then uses this trajectory to assess the threat posed to the vehicle and intervenes in proportion to this threat. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. Simulated results are presented here to demonstrate the framework's ability to incorporate multiple threat thresholds and configurable intervention laws while sharing control with a human driver. 2010-09-30T21:28:54Z 2010-09-30T21:28:54Z 2009-10 Article http://purl.org/eprint/type/JournalArticle 978-1-4244-2793-2 1062-922X INSPEC Accession Number: 11004504 http://hdl.handle.net/1721.1/58809 Anderson, S.J. et al. “A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios.” Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on. 2009. 2032-2037. © 2009, IEEE en_US http://dx.doi.org/10.1109/ICSMC.2009.5346330 IEEE International Conference on Systems, Man and Cybernetics Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | MPC Semi-autonomous control Active safety Hazard avoidance Human-machine interaction Lane keeping Mobile robotics Model predictive control Shared adaptive control Threat assessment Vehicle autonomy Vehicle safety Iagnemma, Karl Peters, Steven Conrad Anderson, Sterling J. Pilutti, Tom E. A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios |
| title | A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios |
| title_full | A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios |
| title_fullStr | A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios |
| title_full_unstemmed | A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios |
| title_short | A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios |
| title_sort | unified approach to semi autonomous control of passenger vehicles in hazard avoidance scenarios |
| topic | MPC Semi-autonomous control Active safety Hazard avoidance Human-machine interaction Lane keeping Mobile robotics Model predictive control Shared adaptive control Threat assessment Vehicle autonomy Vehicle safety |
| url | http://hdl.handle.net/1721.1/58809 |
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