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Risk-aware motion planning for automated vehicle among human-driven cars
Abstract We consider the maneuver planning problem …
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.
viors with different levels of robustness.  +
Authors Jin I. Ge, Bastian Schurmann, Richard M. Murray, and Matthias Althoff  +
Funding VeHICaL: Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems +
ID 2018d  +
Source Submitted, 2019 American Control Conference (ACC)  +
Tag gsma19-acc  +
Title Risk-aware motion planning for automated vehicle among human-driven cars +
Type Conference paper  +
Categories Papers
Modification date
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8 October 2018 05:49:28  +
URL
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http://www.cds.caltech.edu/~murray/preprints/gsma19-acc_s.pdf  +
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Risk-aware motion planning for automated vehicle among human-driven cars + Title
 

 

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