Contingency model predictive control for automated vehicles

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In the field of vehicle automation, contingent events are potential hazards that can be long-tail and high-severity. Their contextual warnings, however, enable contingency planning techniques to mitigate their risks. This dissertation presents Contingency Model Predictive Control (CMPC), a motion planning and control framework that optimizes performance objectives while simultaneously maintaining a contingency plan -- an alternate trajectory to avoid a potential hazard. By preserving the existence of a feasible avoidance trajectory, CMPC anticipates emergency and keeps the controlled system in a safe state that is selectively robust to the identified hazard. This is accomplished by augmenting the typical Model Predictive Control (MPC) prediction horizon with a second horizon, which is constrained to ensure safety from the contingent threat and is coupled to the nominal horizon at its first command. Thus, the two horizons negotiate to compute a command that is both optimized for nominal performance and robust to the contingent event. This dissertation develops several CMPC formulations and demonstrates their effectiveness in simulated and experimental applications. First, a General CMPC is introduced with maximum flexibility for arbitrary application. This general form is then honed to a Linear CMPC with a convex optimization that can converge in real-time. Linear CMPC is simulated on a toy problem and then experimentally demonstrated to control the steering angle of two automated vehicle platforms in obstacle avoidance and loss of tire-road friction scenarios. Finally, a Nonlinear CMPC is developed that controls a vehicle's steering, propulsion, and braking inputs, and is demonstrated experimentally in vehicle-following and loss-of-friction applications. In each demonstration, CMPC is presented in contrast to a Naive MPC and a Worst-Case Robust MPC to illustrate its properties. Analysis of the experiments and simulations herein reveal that Contingency MPC approaches potential emergencies with safe, intuitive, and interpretable behavior that balances conservatism with incentive for high performance operation.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2023; ©2023
Publication date 2023; 2023
Issuance monographic
Language English


Author Alsterda, John Patrick
Degree supervisor Gerdes, J. Christian
Thesis advisor Gerdes, J. Christian
Thesis advisor Boyd, Stephen P
Thesis advisor Kochenderfer, Mykel J, 1980-
Degree committee member Boyd, Stephen P
Degree committee member Kochenderfer, Mykel J, 1980-
Associated with Stanford University, School of Engineering
Associated with Stanford University, Department of Mechanical Engineering


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility John Patrick Alsterda.
Note Submitted to the Department of Mechanical Engineering.
Thesis Thesis Ph.D. Stanford University 2023.

Access conditions

© 2023 by John Patrick Alsterda
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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