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Risk-aware motion planning for automated vehicle among human-driven cars

Ge, Jin I. and Schürmann, Bastian and Murray, Richard M. and Althoff, Matthias (2019) Risk-aware motion planning for automated vehicle among human-driven cars. In: 2019 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 3987-3993. ISBN 978-1-5386-7926-5.

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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 higher-level planning that is robust against lower-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 consisting of an interactive human driving model and risk-aware motion planning strategy makes it possible to adapt to different traffic conditions and confidence levels.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Ge, Jin I.0000-0001-6429-9337
Murray, Richard M.0000-0002-5785-7481
Additional Information:© 2019 AACC. This work is supported by NSF VeHiCal project (Grant Number 1545126) and by the European Commission UnCoVerCPS project (Grant Number 643921).
Group:Center for Autonomous Systems and Technologies (CAST)
Funding AgencyGrant Number
European Research Council (ERC)643921
Record Number:CaltechAUTHORS:20190905-152940342
Persistent URL:
Official Citation:J. I. Ge, B. Schürmann, R. M. Murray and M. Althoff, "Risk-aware motion planning for automated vehicle among human-driven cars," 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 3987-3993. URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98446
Deposited By: Tony Diaz
Deposited On:05 Sep 2019 22:36
Last Modified:25 Oct 2019 19:31

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