Parameter-Conditioned Reachable Sets for Updating Safety Assurances Online

Borquez, Javier; Nakamura, Kensuke; Bansal, Somil

doi:10.1109/icra48891.2023.10160554

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Alternatively, recent works have turned to online estimation of human intent to adapt the reachable set to correspond to the estimated human intent model [22]- [24], and therefore improve the soundness of the reachable set. However, these methods primarily focus on estimating parameters describing a human behavior prediction model to help reduce the overconservatism in an AV's planning algorithm.…”

Section: Related Workmentioning

confidence: 99%

Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition

Topan¹,

Chen²,

Szczerbicki³

et al. 2023

2023 IEEE Intelligent Vehicles Symposium (IV)

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A critical task for developing safe autonomous driving stacks is to determine whether an obstacle is safetycritical, i.e., poses an imminent threat to the autonomous vehicle. Our previous work showed that Hamilton Jacobi reachability theory can be applied to compute interactiondynamics-aware perception safety zones that better inform an ego vehicle's perception module which obstacles are considered safety-critical. For completeness, these zones are typically larger than absolutely necessary, forcing the perception module to pay attention to a larger collection of objects for the sake of conservatism. As an improvement, we propose a maneuverbased decomposition of our safety zones that leverages information about the ego maneuver to reduce the zone volume. In particular, we propose a "temporal convolution" operation that produces safety zones for specific ego maneuvers, thus limiting the ego's behavior to reduce the size of the safety zones. We show with numerical experiments that maneuver-based zones are significantly smaller (up to 76% size reduction) than the baseline while maintaining completeness.

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Section: Related Workmentioning

confidence: 99%