Pith Number

pith:UP6K5VDS

pith:2026:UP6K5VDS5DY4HRWCNGGED3OL53

not attested not anchored not stored refs resolved

DriveSafer: End-to-End Autonomous Driving with Safety Guidance

Raj Rajkumar, Shounak Sural

A safety framework for end-to-end driving planners cuts catastrophic failures by 48 percent on the NAVSIM benchmark.

arxiv:2605.16737 v1 · 2026-05-16 · cs.RO · cs.CV

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Compared to the state-of-the-art DiffusionDrive model, on the NAVSIM benchmark, DriveSafer reduces the number of catastrophic failures (PDMS=0) by 48%, with over 65% reduction in drivable-area compliance failures.

C2weakest assumption

The claim that many catastrophic failures arise specifically from violations of physical constraints and safety requirements (abstract, paragraph beginning 'We find that many such failures arise...') and that adding training-time constraints plus inference-time guidance will reduce those failures without creating new failure modes or degrading performance on non-catastrophic cases.

C3one line summary

DriveSafer reduces catastrophic failures (PDMS=0) by 48% and drivable-area compliance failures by over 65% versus DiffusionDrive on the NAVSIM benchmark by combining training-time safety constraints with inference-time guidance.

References

44 extracted · 44 resolved · 8 Pith anchors

[1] Real-time mpc with control barrier functions for autonomous driving using safety enhanced col- location.IFAC-PapersOnLine, 58(18):392–399, 2024 2024

[2] NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles 2021 · arXiv:2106.11810

[3] arXiv preprint arXiv:2506.04218 (2025)

[4] Learning by cheating 2020

[5] VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning · arXiv:2402.13243

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:02:39.100884Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a3fcaed472e8f1c3c6c2698c41edcbeed127afc249b0e4f926a810e16d325493

Aliases

arxiv: 2605.16737 · arxiv_version: 2605.16737v1 · doi: 10.48550/arxiv.2605.16737 · pith_short_12: UP6K5VDS5DY4 · pith_short_16: UP6K5VDS5DY4HRWC · pith_short_8: UP6K5VDS

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UP6K5VDS5DY4HRWCNGGED3OL53 \
  | 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: a3fcaed472e8f1c3c6c2698c41edcbeed127afc249b0e4f926a810e16d325493

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "741ea1507ab4e1fc8dbab4c7c0a4f938fc986e358fe7e94a54af2f8dd9ed04d3",
    "cross_cats_sorted": [
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-16T01:21:30Z",
    "title_canon_sha256": "4b2e0e77a6dc93d6f62c7ff34bf4ca210f049b124b8bdd56564f10c85b70dcdc"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16737",
    "kind": "arxiv",
    "version": 1
  }
}