{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:OHWF4I4TYR4Q2YGBDH4WJAKMLM","short_pith_number":"pith:OHWF4I4T","canonical_record":{"source":{"id":"2106.11810","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-22T14:24:55Z","cross_cats_sorted":[],"title_canon_sha256":"dc5b7ba36498f684b928c4f9cd994b1b27aeaa522c3c1ce6be58b18844e67273","abstract_canon_sha256":"5bf6c22d6748d61fbbb4dad4fb81fd0042769c071faa2e8b3bb4ee792f2a18ee"},"schema_version":"1.0"},"canonical_sha256":"71ec5e2393c4790d60c119f964814c5b102dda94ab6c4e7ff0173c5c8d7a440d","source":{"kind":"arxiv","id":"2106.11810","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.11810","created_at":"2026-05-17T23:38:52Z"},{"alias_kind":"arxiv_version","alias_value":"2106.11810v4","created_at":"2026-05-17T23:38:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.11810","created_at":"2026-05-17T23:38:52Z"},{"alias_kind":"pith_short_12","alias_value":"OHWF4I4TYR4Q","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"OHWF4I4TYR4Q2YGB","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"OHWF4I4T","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:OHWF4I4TYR4Q2YGBDH4WJAKMLM","target":"record","payload":{"canonical_record":{"source":{"id":"2106.11810","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-22T14:24:55Z","cross_cats_sorted":[],"title_canon_sha256":"dc5b7ba36498f684b928c4f9cd994b1b27aeaa522c3c1ce6be58b18844e67273","abstract_canon_sha256":"5bf6c22d6748d61fbbb4dad4fb81fd0042769c071faa2e8b3bb4ee792f2a18ee"},"schema_version":"1.0"},"canonical_sha256":"71ec5e2393c4790d60c119f964814c5b102dda94ab6c4e7ff0173c5c8d7a440d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:52.947499Z","signature_b64":"a/4TI7y4u/uo2WrGvRz1ivtTZwxsbEEcsLxl7zUbMj7tuWANcmjXvLsrAyEEg71Vc2FkoWSFaUzOLnGIEDxHBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71ec5e2393c4790d60c119f964814c5b102dda94ab6c4e7ff0173c5c8d7a440d","last_reissued_at":"2026-05-17T23:38:52.946943Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:52.946943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.11810","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nbtCcgLzKZ7jtBYQ83ro4CMA7+zDJHHNFeUQvJASVCXrVQwi6O4sSTlek/e4TIfibVtdCunTNytI0MxIL6B/Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T23:56:29.491808Z"},"content_sha256":"5e1f83bf38455db8304227e7000e50bb856f0b0a9c74d433c30caaa96ce94d01","schema_version":"1.0","event_id":"sha256:5e1f83bf38455db8304227e7000e50bb856f0b0a9c74d433c30caaa96ce94d01"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:OHWF4I4TYR4Q2YGBDH4WJAKMLM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"NuPlan establishes the first closed-loop benchmark for machine learning planners in autonomous driving.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alex Lang, Eric Wolff, Holger Caesar, Juraj Kabzan, Kok Seang Tan, Luke Fletcher, Oscar Beijbom, Sammy Omari, Whye Kit Fong","submitted_at":"2021-06-22T14:24:55Z","abstract_excerpt":"In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving. While there is a growing body of ML-based motion planners, the lack of established datasets and metrics has limited the progress in this area. Existing benchmarks for autonomous vehicle motion prediction have focused on short-term motion forecasting, rather than long-term planning. This has led previous works to use open-loop evaluation with L2-based metrics, which are not suitable for fairly evaluating long-term planning. Our benchmark overcomes these limitations by introducing a large-s"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the chosen metrics and reactive-agent simulator will produce rankings that correlate with real-world safety and performance once deployed on physical vehicles.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"NuPlan is the first closed-loop benchmark for ML-based autonomous vehicle planning, with 1500h multi-city driving data, reactive simulation, and scenario-specific metrics.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"NuPlan establishes the first closed-loop benchmark for machine learning planners in autonomous driving.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"975e888e3c7661148de1dda04310d4319b4febc572c6fa8b77eff698bdf1096e"},"source":{"id":"2106.11810","kind":"arxiv","version":4},"verdict":{"id":"93df7ad5-d461-4b9d-af8d-a84d91ccee7a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T08:57:39.837798Z","strongest_claim":"In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving.","one_line_summary":"NuPlan is the first closed-loop benchmark for ML-based autonomous vehicle planning, with 1500h multi-city driving data, reactive simulation, and scenario-specific metrics.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the chosen metrics and reactive-agent simulator will produce rankings that correlate with real-world safety and performance once deployed on physical vehicles.","pith_extraction_headline":"NuPlan establishes the first closed-loop benchmark for machine learning planners in autonomous driving."},"references":{"count":24,"sample":[{"doi":"","year":2017,"title":"CommonRoad: Composable benchmarks for motion plan- ning on roads","work_id":"25af57e2-ac60-41a9-bded-f3495a3658f9","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Chauf- feurnet: Learning to drive by imitating the best and synthe- sizing the worst","work_id":"de43fcae-ee6a-49c5-b229-01a0ce8e9804","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Learning to drive from simulation without real world labels","work_id":"6a3833a4-2967-48ba-b3df-f714fddced35","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom","work_id":"de934989-2ff8-49f5-a3b3-0fe39f4a14c2","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"MP3: A uniﬁed model to map, perceive, predict and plan","work_id":"ffea778c-e8d9-4a0e-b307-aed0f162ae7d","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":24,"snapshot_sha256":"c44d7de0fc6611a2c42f361a188d449d6663762170b4e5f8b94b9bb1332021e3","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"415d64bdf6099cc2c8f1e608edf4435de561ce19819a59cd2a2953729d095917"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"93df7ad5-d461-4b9d-af8d-a84d91ccee7a"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g3x/FHSr/4UK73h2rygRC+z8zT39qCci/UclOdUVAjG4aZvPMwkukUoH2HsSYQQ09h48NrrmAzOrQ9vDPu6zDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T23:56:29.492577Z"},"content_sha256":"3ab29177db77b6cc842f34adaeee7fd0a126dd217697c04996f8fdcdcd17655f","schema_version":"1.0","event_id":"sha256:3ab29177db77b6cc842f34adaeee7fd0a126dd217697c04996f8fdcdcd17655f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OHWF4I4TYR4Q2YGBDH4WJAKMLM/bundle.json","state_url":"https://pith.science/pith/OHWF4I4TYR4Q2YGBDH4WJAKMLM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OHWF4I4TYR4Q2YGBDH4WJAKMLM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-23T23:56:29Z","links":{"resolver":"https://pith.science/pith/OHWF4I4TYR4Q2YGBDH4WJAKMLM","bundle":"https://pith.science/pith/OHWF4I4TYR4Q2YGBDH4WJAKMLM/bundle.json","state":"https://pith.science/pith/OHWF4I4TYR4Q2YGBDH4WJAKMLM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OHWF4I4TYR4Q2YGBDH4WJAKMLM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:OHWF4I4TYR4Q2YGBDH4WJAKMLM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5bf6c22d6748d61fbbb4dad4fb81fd0042769c071faa2e8b3bb4ee792f2a18ee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-22T14:24:55Z","title_canon_sha256":"dc5b7ba36498f684b928c4f9cd994b1b27aeaa522c3c1ce6be58b18844e67273"},"schema_version":"1.0","source":{"id":"2106.11810","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.11810","created_at":"2026-05-17T23:38:52Z"},{"alias_kind":"arxiv_version","alias_value":"2106.11810v4","created_at":"2026-05-17T23:38:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.11810","created_at":"2026-05-17T23:38:52Z"},{"alias_kind":"pith_short_12","alias_value":"OHWF4I4TYR4Q","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"OHWF4I4TYR4Q2YGB","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"OHWF4I4T","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:3ab29177db77b6cc842f34adaeee7fd0a126dd217697c04996f8fdcdcd17655f","target":"graph","created_at":"2026-05-17T23:38:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the chosen metrics and reactive-agent simulator will produce rankings that correlate with real-world safety and performance once deployed on physical vehicles."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"NuPlan is the first closed-loop benchmark for ML-based autonomous vehicle planning, with 1500h multi-city driving data, reactive simulation, and scenario-specific metrics."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"NuPlan establishes the first closed-loop benchmark for machine learning planners in autonomous driving."}],"snapshot_sha256":"975e888e3c7661148de1dda04310d4319b4febc572c6fa8b77eff698bdf1096e"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"415d64bdf6099cc2c8f1e608edf4435de561ce19819a59cd2a2953729d095917"},"paper":{"abstract_excerpt":"In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving. While there is a growing body of ML-based motion planners, the lack of established datasets and metrics has limited the progress in this area. Existing benchmarks for autonomous vehicle motion prediction have focused on short-term motion forecasting, rather than long-term planning. This has led previous works to use open-loop evaluation with L2-based metrics, which are not suitable for fairly evaluating long-term planning. Our benchmark overcomes these limitations by introducing a large-s","authors_text":"Alex Lang, Eric Wolff, Holger Caesar, Juraj Kabzan, Kok Seang Tan, Luke Fletcher, Oscar Beijbom, Sammy Omari, Whye Kit Fong","cross_cats":[],"headline":"NuPlan establishes the first closed-loop benchmark for machine learning planners in autonomous driving.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-22T14:24:55Z","title":"NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles"},"references":{"count":24,"internal_anchors":0,"resolved_work":24,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"CommonRoad: Composable benchmarks for motion plan- ning on roads","work_id":"25af57e2-ac60-41a9-bded-f3495a3658f9","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Chauf- feurnet: Learning to drive by imitating the best and synthe- sizing the worst","work_id":"de43fcae-ee6a-49c5-b229-01a0ce8e9804","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Learning to drive from simulation without real world labels","work_id":"6a3833a4-2967-48ba-b3df-f714fddced35","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom","work_id":"de934989-2ff8-49f5-a3b3-0fe39f4a14c2","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"MP3: A uniﬁed model to map, perceive, predict and plan","work_id":"ffea778c-e8d9-4a0e-b307-aed0f162ae7d","year":null}],"snapshot_sha256":"c44d7de0fc6611a2c42f361a188d449d6663762170b4e5f8b94b9bb1332021e3"},"source":{"id":"2106.11810","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-15T08:57:39.837798Z","id":"93df7ad5-d461-4b9d-af8d-a84d91ccee7a","model_set":{"reader":"grok-4.3"},"one_line_summary":"NuPlan is the first closed-loop benchmark for ML-based autonomous vehicle planning, with 1500h multi-city driving data, reactive simulation, and scenario-specific metrics.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"NuPlan establishes the first closed-loop benchmark for machine learning planners in autonomous driving.","strongest_claim":"In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving.","weakest_assumption":"That the chosen metrics and reactive-agent simulator will produce rankings that correlate with real-world safety and performance once deployed on physical vehicles."}},"verdict_id":"93df7ad5-d461-4b9d-af8d-a84d91ccee7a"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5e1f83bf38455db8304227e7000e50bb856f0b0a9c74d433c30caaa96ce94d01","target":"record","created_at":"2026-05-17T23:38:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5bf6c22d6748d61fbbb4dad4fb81fd0042769c071faa2e8b3bb4ee792f2a18ee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-22T14:24:55Z","title_canon_sha256":"dc5b7ba36498f684b928c4f9cd994b1b27aeaa522c3c1ce6be58b18844e67273"},"schema_version":"1.0","source":{"id":"2106.11810","kind":"arxiv","version":4}},"canonical_sha256":"71ec5e2393c4790d60c119f964814c5b102dda94ab6c4e7ff0173c5c8d7a440d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"71ec5e2393c4790d60c119f964814c5b102dda94ab6c4e7ff0173c5c8d7a440d","first_computed_at":"2026-05-17T23:38:52.946943Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:52.946943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a/4TI7y4u/uo2WrGvRz1ivtTZwxsbEEcsLxl7zUbMj7tuWANcmjXvLsrAyEEg71Vc2FkoWSFaUzOLnGIEDxHBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:52.947499Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.11810","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e1f83bf38455db8304227e7000e50bb856f0b0a9c74d433c30caaa96ce94d01","sha256:3ab29177db77b6cc842f34adaeee7fd0a126dd217697c04996f8fdcdcd17655f"],"state_sha256":"16611583970a8076250dc9962b0e0c6ef901f0adc12f65623125b9b596bc40f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Z8sThv37OMz7qTvVWsV8BbnxDMC3s3XcWvLUbaEmv75cLEiTgC1BAE6DIoJZA1zxHaM4f7Ajg9ufqeuFAtxDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T23:56:29.498371Z","bundle_sha256":"c1e9d689d5ecb545c2aec8c6667c49018c657a8c8831c1769a62fcd2a27a0f66"}}