Sampling-based threat assessment algorithms for intersection collisions involving errant drivers

Abstract

  This paper considers the decision-making problem for a vehicle crossing a road intersection in the presence of other, potentially errant, drivers. This problem is considered in a game-theoretic framework, where the errant drivers are assumed to be capable of causing intentional collisions. Our approach is to simulate the possible behaviors of errant drivers using RRT-Reach, a modified application of rapidly-exploring random trees. A novelty in RRT-Reach is the use of a dual exploration-pursuit mode, which allows for efficient approximation of the errant reachability set for some fixed time horizon. Through simulation and experimental results with a small autonomous vehicle, we demonstrate that this threat assessment algorithm can be used in real-time to minimize the risk of collision.

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The different versions of the original document can be found in:

• http://acl.mit.edu/papers/Aoude10_IAV.pdf

• http://acl.mit.edu/papers/Aoude10_IAV.pdf,

https://www.sciencedirect.com/science/article/pii/S1474667016351205,

https://dspace.mit.edu/openaccess-disseminate/1721.1/60041,

https://academic.microsoft.com/#/detail/2023815713

• http://hdl.handle.net/1721.1/60041,

https://orcid.org/0000-0001-8576-1930 under the license http://creativecommons.org/licenses/by-nc-sa/3.0/

• https://api.elsevier.com/content/article/PII:S1474667016351205?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S1474667016351205?httpAccept=text/plain,

http://dx.doi.org/10.3182/20100906-3-it-2019.00100 under the license https://www.elsevier.com/tdm/userlicense/1.0/

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