Risk-aware motion planning for automated vehicle among human-driven cars
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| Title | Risk-aware motion planning for automated vehicle among human-driven cars |
|---|---|
| Authors | Jin I. Ge, Bastian Schurmann, Richard M. Murray and and Matthias Althoff |
| Source | Submitted, 2019 American Control Conference (ACC) |
| Abstract | We consider the maneuver planning problem for automated vehicles when they share the road with human- driven cars and interact with each other using a finite set of maneuvers. Each maneuver is calculated considering input constraints, actuator disturbances and sensor noise, so that we can use a maneuver automaton to perform high-level planning that is robust against low-level effects. In order to model the behavior of human-driven cars in response to the intent of the automated vehicle, we use control improvisation to build a probabilistic model. To accommodate for potential mismatches between the learned human model and human driving behaviors, we use a conditional value-at-risk objective function to obtain the optimal policy for the automated vehicle. We demonstrate through simulations that our motion planning framework allows an automated vehicle to exploit human behaviors with different levels of robustness. |
| Type | Conference paper |
| URL | http://www.cds.caltech.edu/~murray/preprints/gsma19-acc s.pdf |
| DOI | |
| Tag | gsma19-acc |
| ID | 2018d |
| Funding | NSF VeHICaL |
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