A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios
Author(s)
Iagnemma, Karl; Peters, Steven Conrad; Anderson, Sterling J.; Pilutti, Tom E.
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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.
Date issued
2009-10Department
Massachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Laboratory for Manufacturing and ProductivityJournal
IEEE International Conference on Systems, Man and Cybernetics
Publisher
Institute of Electrical and Electronics Engineers
Citation
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
Version: Final published version
Other identifiers
INSPEC Accession Number: 11004504
ISBN
978-1-4244-2793-2
ISSN
1062-922X
Keywords
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