{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:HAQZCDMQNFCCQSPLXKA3F6GSGU","short_pith_number":"pith:HAQZCDMQ","schema_version":"1.0","canonical_sha256":"3821910d9069442849ebba81b2f8d2351be60118db6faea64ea3d3426d3895fc","source":{"kind":"arxiv","id":"2010.09350","version":3},"attestation_state":"computed","paper":{"title":"The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Holger Caesar, Jonah Philion, Oscar Beijbom, Sanja Fidler, Yiluan Guo","submitted_at":"2020-10-19T09:32:48Z","abstract_excerpt":"A high-performing object detection system plays a crucial role in autonomous driving (AD). The performance, typically evaluated in terms of mean Average Precision, does not take into account orientation and distance of the actors in the scene, which are important for the safe AD. It also ignores environmental context. Recently, Philion et al. proposed a neural planning metric (PKL), based on the KL divergence of a planner's trajectory and the groundtruth route, to accommodate these requirements. In this paper, we use this neural planning metric to score all submissions of the nuScenes detectio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2010.09350","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-10-19T09:32:48Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"3124547ffd51ad5ae4461f48e58e5860b6dcb7cca954ceef51102f19a40fd118","abstract_canon_sha256":"a26a8e9a4ef84818c202f0395a63ba2cccc577f8b86eead7b0d5097881d98fc3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:57:09.918833Z","signature_b64":"JpV4jjePdaikjo0vWgaF5IY4rUxN+mdd+LFPiZxpBbL7Krw1MApGIo4yxE5ZbGNPUp2G7CziGFzkTK0/cEq2Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3821910d9069442849ebba81b2f8d2351be60118db6faea64ea3d3426d3895fc","last_reissued_at":"2026-07-05T02:57:09.918310Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:57:09.918310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Holger Caesar, Jonah Philion, Oscar Beijbom, Sanja Fidler, Yiluan Guo","submitted_at":"2020-10-19T09:32:48Z","abstract_excerpt":"A high-performing object detection system plays a crucial role in autonomous driving (AD). The performance, typically evaluated in terms of mean Average Precision, does not take into account orientation and distance of the actors in the scene, which are important for the safe AD. It also ignores environmental context. Recently, Philion et al. proposed a neural planning metric (PKL), based on the KL divergence of a planner's trajectory and the groundtruth route, to accommodate these requirements. In this paper, we use this neural planning metric to score all submissions of the nuScenes detectio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.09350","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.09350/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2010.09350","created_at":"2026-07-05T02:57:09.918375+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.09350v3","created_at":"2026-07-05T02:57:09.918375+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.09350","created_at":"2026-07-05T02:57:09.918375+00:00"},{"alias_kind":"pith_short_12","alias_value":"HAQZCDMQNFCC","created_at":"2026-07-05T02:57:09.918375+00:00"},{"alias_kind":"pith_short_16","alias_value":"HAQZCDMQNFCCQSPL","created_at":"2026-07-05T02:57:09.918375+00:00"},{"alias_kind":"pith_short_8","alias_value":"HAQZCDMQ","created_at":"2026-07-05T02:57:09.918375+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00283","citing_title":"What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU","json":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU.json","graph_json":"https://pith.science/api/pith-number/HAQZCDMQNFCCQSPLXKA3F6GSGU/graph.json","events_json":"https://pith.science/api/pith-number/HAQZCDMQNFCCQSPLXKA3F6GSGU/events.json","paper":"https://pith.science/paper/HAQZCDMQ"},"agent_actions":{"view_html":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU","download_json":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU.json","view_paper":"https://pith.science/paper/HAQZCDMQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.09350&json=true","fetch_graph":"https://pith.science/api/pith-number/HAQZCDMQNFCCQSPLXKA3F6GSGU/graph.json","fetch_events":"https://pith.science/api/pith-number/HAQZCDMQNFCCQSPLXKA3F6GSGU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU/action/storage_attestation","attest_author":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU/action/author_attestation","sign_citation":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU/action/citation_signature","submit_replication":"https://pith.science/pith/HAQZCDMQNFCCQSPLXKA3F6GSGU/action/replication_record"}},"created_at":"2026-07-05T02:57:09.918375+00:00","updated_at":"2026-07-05T02:57:09.918375+00:00"}