[2605.12710] Belief-Space Residual Risk for Automated Driving under Localization Uncertainty
[Submitted on 12 May 2026]
Title:Belief-Space Residual Risk for Automated Driving under Localization Uncertainty
View a PDF of the paper titled Belief-Space Residual Risk for Automated Driving under Localization Uncertainty, by Nijinshan Karunainayagam and 2 other authors
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Abstract:Residual risk metrics have recently been introduced to assess the safety implications of automated driving systems. Existing approaches typically assume a deterministic ego pose and concentrate mainly on perception errors related to surrounding objects and latency effects. In practice, however, automated vehicles operate under considerable localization uncertainty, especially in complex urban settings and in adverse weather conditions. This work extends the spatial residual risk formulation to the belief space by explicitly modeling ego pose uncertainty as a Gaussian distribution. Residual risk is reformulated as the expected degradation-induced risk over the ego pose belief distribution. Within a particle-based risk estimation framework, localization uncertainty is incorporated into the computation of collision probabilities through covariance fusion of ego and object uncertainties.
Comments: 7 Pages, this work has been accepted for publication in IEEE Intelligent Transportation Systems (ITSC) 2026. The final published version will be available via IEEE Xplore
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.12710 [cs.RO]
(or arXiv:2605.12710v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.12710
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Nijinshan Karunainayagam [view email] [v1] Tue, 12 May 2026 20:16:52 UTC (14,269 KB)
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