{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2ZECIIXWJS76EVIUW6LLAVHN5L","short_pith_number":"pith:2ZECIIXW","schema_version":"1.0","canonical_sha256":"d6482422f64cbfe25514b796b054edead33b0183df5da2ea1f8609f89749d313","source":{"kind":"arxiv","id":"2304.01168","version":5},"attestation_state":"computed","paper":{"title":"DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Chongjian Ge, Enze Xie, Junsong Chen, Ping Luo, Sukmin Kim, Tianqi Wang, Wenxuan Ji, Zhenguo Li","submitted_at":"2023-04-03T17:37:00Z","abstract_excerpt":"Safety is the primary priority of autonomous driving. Nevertheless, no published dataset currently supports the direct and explainable safety evaluation for autonomous driving. In this work, we propose DeepAccident, a large-scale dataset generated via a realistic simulator containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset with 40k annotated samples. In addition, we propose a new task, end-to-end motion and "},"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":"2304.01168","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-03T17:37:00Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"1584c80469bead0820adf161abadeaf4970d701abd6998daff5d4ef6a68c2935","abstract_canon_sha256":"07a007cb5509bd73a5ecf13096fe039de7740a6befcc94b568173d94cd1b418b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:49.321221Z","signature_b64":"pF3n4psS3PcoO5XZ/4sELomn6NmxYo+mcZyixz8fWk3qnMSwEhUUJ0HhipUU9EFctYyU4hf8YyDZEk/CyyDKAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6482422f64cbfe25514b796b054edead33b0183df5da2ea1f8609f89749d313","last_reissued_at":"2026-07-05T07:24:49.320720Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:49.320720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Chongjian Ge, Enze Xie, Junsong Chen, Ping Luo, Sukmin Kim, Tianqi Wang, Wenxuan Ji, Zhenguo Li","submitted_at":"2023-04-03T17:37:00Z","abstract_excerpt":"Safety is the primary priority of autonomous driving. Nevertheless, no published dataset currently supports the direct and explainable safety evaluation for autonomous driving. In this work, we propose DeepAccident, a large-scale dataset generated via a realistic simulator containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset with 40k annotated samples. In addition, we propose a new task, end-to-end motion and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.01168","kind":"arxiv","version":5},"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/2304.01168/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":"2304.01168","created_at":"2026-07-05T07:24:49.320786+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.01168v5","created_at":"2026-07-05T07:24:49.320786+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.01168","created_at":"2026-07-05T07:24:49.320786+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZECIIXWJS76","created_at":"2026-07-05T07:24:49.320786+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZECIIXWJS76EVIU","created_at":"2026-07-05T07:24:49.320786+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZECIIXW","created_at":"2026-07-05T07:24:49.320786+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.11551","citing_title":"CarlaNCAP: A Framework for Quantifying the Safety of Vulnerable Road Users in Infrastructure-Assisted Collective Perception Using EuroNCAP Scenarios","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L","json":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L.json","graph_json":"https://pith.science/api/pith-number/2ZECIIXWJS76EVIUW6LLAVHN5L/graph.json","events_json":"https://pith.science/api/pith-number/2ZECIIXWJS76EVIUW6LLAVHN5L/events.json","paper":"https://pith.science/paper/2ZECIIXW"},"agent_actions":{"view_html":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L","download_json":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L.json","view_paper":"https://pith.science/paper/2ZECIIXW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.01168&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZECIIXWJS76EVIUW6LLAVHN5L/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZECIIXWJS76EVIUW6LLAVHN5L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L/action/storage_attestation","attest_author":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L/action/author_attestation","sign_citation":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L/action/citation_signature","submit_replication":"https://pith.science/pith/2ZECIIXWJS76EVIUW6LLAVHN5L/action/replication_record"}},"created_at":"2026-07-05T07:24:49.320786+00:00","updated_at":"2026-07-05T07:24:49.320786+00:00"}