An anticipatory dynamic bayesian network approach towards an autonomous vehicle safety reasoning system

Philip A. Frederick; Kac Cheok; Mike Del Rose; Robert Kania

doi:10.1117/12.2616780

31 May 2022 An anticipatory dynamic bayesian network approach towards an autonomous vehicle safety reasoning system

Philip A. Frederick, Kac Cheok, Mike Del Rose, Robert Kania

Proceedings Volume 12124, Unmanned Systems Technology XXIV; 1212407 (2022) https://doi.org/10.1117/12.2616780
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Despite numerous investments toward autonomous vehicle technology this past decade the ensured safe operation of these systems is still an unresolved issue for both commercial and defense systems due to decision uncertainty. In complex dynamic domains (e.g. intersections or congested terrain) the expected mode of operation for ensured safety of these unmanned systems is still direct human control (whether through direct vehicle input or through teleoperation). This paper presents research toward an autonomous vehicle safety reasoning system that provides a novel approach to temporally address scene uncertainty to increase the safety envelope for commercial and defense systems.

Citation Download Citation

Philip A. Frederick, Kac Cheok, Mike Del Rose, and Robert Kania "An anticipatory dynamic bayesian network approach towards an autonomous vehicle safety reasoning system", Proc. SPIE 12124, Unmanned Systems Technology XXIV, 1212407 (31 May 2022); https://doi.org/10.1117/12.2616780

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