Pith Number

pith:DBN6PDFN

pith:2026:DBN6PDFNQVFXCTOIIGZMYGHZQJ

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Belief-Space Residual Risk for Automated Driving under Localization Uncertainty

Frank Diermeyer, Nijinshan Karunainayagam, Nils Gehrke

Residual risk assessment for automated driving is extended into belief space by modeling ego pose uncertainty as a Gaussian distribution and reformulating risk as an expectation over that belief.

arxiv:2605.12710 v1 · 2026-05-12 · cs.RO

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Claims

C1strongest claim

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.

C2weakest assumption

That ego pose uncertainty is adequately captured by a single Gaussian and that covariance fusion of ego and object uncertainties produces accurate collision probabilities within the particle-based framework.

C3one line summary

Reformulates residual risk as the expected value over a Gaussian ego-pose belief distribution and incorporates it into particle-based collision probabilities via covariance fusion.

References

23 extracted · 23 resolved · 0 Pith anchors

[1] Road vehicles – Functional safety, 2011

[2] Is it safe to drive? an overview of factors, metrics, and datasets for driveability assessment in au- tonomous driving, 2020

[3] State Estimation Based on Generalized Gaussian Distributions, 2013

[4] Tumdot–muc: Data collection and processing of multimodal trajectories collected by aerial drones, 2024

[5] Introducing spatial residual risk for information degradation in automated driving, 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:49.556707Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

185be78cad854b714dc841b2cc18f982468cb19c1e4de5ca3e1fce73355fa294

Aliases

arxiv: 2605.12710 · arxiv_version: 2605.12710v1 · doi: 10.48550/arxiv.2605.12710 · pith_short_12: DBN6PDFNQVFX · pith_short_16: DBN6PDFNQVFXCTOI · pith_short_8: DBN6PDFN

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DBN6PDFNQVFXCTOIIGZMYGHZQJ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 185be78cad854b714dc841b2cc18f982468cb19c1e4de5ca3e1fce73355fa294

Canonical record JSON

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    "abstract_canon_sha256": "4b6d0769b15a9ec1059311e845496a57ee7f72b4f823d3946b8c9b2239e45957",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-12T20:16:52Z",
    "title_canon_sha256": "8ab931a7397046fda9bcc4c4fd1711fb1d48416d5eb6a8c83dd596068d558b79"
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    "kind": "arxiv",
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}