{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:F6JYQNSIM2QOS5F2UOWBCZ57OM","short_pith_number":"pith:F6JYQNSI","canonical_record":{"source":{"id":"2303.12077","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2023-03-21T17:59:22Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a793c2994f9dc866b9e82c41a380fbb5d2cf73ede486cc7e365c72f2ae3025d7","abstract_canon_sha256":"ac929e571e36c8a5557fb559cbbeb9f7ff15b80855e8e88e67b0ffe4bccba78b"},"schema_version":"1.0"},"canonical_sha256":"2f9388364866a0e974baa3ac1167bf7328bef2e4b0e71f3d2b970cc1d3a0378e","source":{"kind":"arxiv","id":"2303.12077","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.12077","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"arxiv_version","alias_value":"2303.12077v3","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.12077","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"pith_short_12","alias_value":"F6JYQNSIM2QO","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"pith_short_16","alias_value":"F6JYQNSIM2QOS5F2","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"pith_short_8","alias_value":"F6JYQNSI","created_at":"2026-07-05T06:44:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:F6JYQNSIM2QOS5F2UOWBCZ57OM","target":"record","payload":{"canonical_record":{"source":{"id":"2303.12077","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2023-03-21T17:59:22Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a793c2994f9dc866b9e82c41a380fbb5d2cf73ede486cc7e365c72f2ae3025d7","abstract_canon_sha256":"ac929e571e36c8a5557fb559cbbeb9f7ff15b80855e8e88e67b0ffe4bccba78b"},"schema_version":"1.0"},"canonical_sha256":"2f9388364866a0e974baa3ac1167bf7328bef2e4b0e71f3d2b970cc1d3a0378e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:44:06.806101Z","signature_b64":"CSSb4We3/C7eT4CCNbW7NHoFDbTw64XDfLALTqjcKws9hs9qzBq10ugV+JYnJbIYs+SWVblYjS7zjHvtyLK3BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f9388364866a0e974baa3ac1167bf7328bef2e4b0e71f3d2b970cc1d3a0378e","last_reissued_at":"2026-07-05T06:44:06.805543Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:44:06.805543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.12077","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:44:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wydUKbxdVzSr7RUwAJ8rRsCzpxeGG5aSj119KGYCN0gonoKcqj42odBwuU8RNjC9JxTBux4S/9dCTvIeM6ZuBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:22:38.872704Z"},"content_sha256":"b3ca8d85298da801f954c6a5025f106362fded8140f97332c48ff0cafb00bbe7","schema_version":"1.0","event_id":"sha256:b3ca8d85298da801f954c6a5025f106362fded8140f97332c48ff0cafb00bbe7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:F6JYQNSIM2QOS5F2UOWBCZ57OM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VAD: Vectorized Scene Representation for Efficient Autonomous Driving","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Bencheng Liao, Bo Jiang, Chang Huang, Helong Zhou, Jiajie Chen, Qian Zhang, Qing Xu, Shaoyu Chen, Wenyu Liu, Xinggang Wang","submitted_at":"2023-03-21T17:59:22Z","abstract_excerpt":"Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the instance-level structure information. In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation. The proposed vectorized paradigm has two significant advantages. On one hand, VAD exploits the vectorized ag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12077","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/2303.12077/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:44:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tdZgMGCbfkovh6N5Jjei9mkGnfRYB//+wg6CAThC5ymfHuQEgr3+wCEB1w6u+CBpSgQCnOKhG7Fpl8HnhHvzAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:22:38.873066Z"},"content_sha256":"3e4d8d07759e0b31463c6b0332a25649450d43d2d11578a3968c0ceee093d0ce","schema_version":"1.0","event_id":"sha256:3e4d8d07759e0b31463c6b0332a25649450d43d2d11578a3968c0ceee093d0ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F6JYQNSIM2QOS5F2UOWBCZ57OM/bundle.json","state_url":"https://pith.science/pith/F6JYQNSIM2QOS5F2UOWBCZ57OM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F6JYQNSIM2QOS5F2UOWBCZ57OM/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":"2026-07-06T20:22:38Z","links":{"resolver":"https://pith.science/pith/F6JYQNSIM2QOS5F2UOWBCZ57OM","bundle":"https://pith.science/pith/F6JYQNSIM2QOS5F2UOWBCZ57OM/bundle.json","state":"https://pith.science/pith/F6JYQNSIM2QOS5F2UOWBCZ57OM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F6JYQNSIM2QOS5F2UOWBCZ57OM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:F6JYQNSIM2QOS5F2UOWBCZ57OM","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":"ac929e571e36c8a5557fb559cbbeb9f7ff15b80855e8e88e67b0ffe4bccba78b","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2023-03-21T17:59:22Z","title_canon_sha256":"a793c2994f9dc866b9e82c41a380fbb5d2cf73ede486cc7e365c72f2ae3025d7"},"schema_version":"1.0","source":{"id":"2303.12077","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.12077","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"arxiv_version","alias_value":"2303.12077v3","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.12077","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"pith_short_12","alias_value":"F6JYQNSIM2QO","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"pith_short_16","alias_value":"F6JYQNSIM2QOS5F2","created_at":"2026-07-05T06:44:06Z"},{"alias_kind":"pith_short_8","alias_value":"F6JYQNSI","created_at":"2026-07-05T06:44:06Z"}],"graph_snapshots":[{"event_id":"sha256:3e4d8d07759e0b31463c6b0332a25649450d43d2d11578a3968c0ceee093d0ce","target":"graph","created_at":"2026-07-05T06:44:06Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2303.12077/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the instance-level structure information. In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation. The proposed vectorized paradigm has two significant advantages. On one hand, VAD exploits the vectorized ag","authors_text":"Bencheng Liao, Bo Jiang, Chang Huang, Helong Zhou, Jiajie Chen, Qian Zhang, Qing Xu, Shaoyu Chen, Wenyu Liu, Xinggang Wang","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2023-03-21T17:59:22Z","title":"VAD: Vectorized Scene Representation for Efficient Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.12077","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b3ca8d85298da801f954c6a5025f106362fded8140f97332c48ff0cafb00bbe7","target":"record","created_at":"2026-07-05T06:44:06Z","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":"ac929e571e36c8a5557fb559cbbeb9f7ff15b80855e8e88e67b0ffe4bccba78b","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2023-03-21T17:59:22Z","title_canon_sha256":"a793c2994f9dc866b9e82c41a380fbb5d2cf73ede486cc7e365c72f2ae3025d7"},"schema_version":"1.0","source":{"id":"2303.12077","kind":"arxiv","version":3}},"canonical_sha256":"2f9388364866a0e974baa3ac1167bf7328bef2e4b0e71f3d2b970cc1d3a0378e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f9388364866a0e974baa3ac1167bf7328bef2e4b0e71f3d2b970cc1d3a0378e","first_computed_at":"2026-07-05T06:44:06.805543Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:44:06.805543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CSSb4We3/C7eT4CCNbW7NHoFDbTw64XDfLALTqjcKws9hs9qzBq10ugV+JYnJbIYs+SWVblYjS7zjHvtyLK3BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:44:06.806101Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.12077","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b3ca8d85298da801f954c6a5025f106362fded8140f97332c48ff0cafb00bbe7","sha256:3e4d8d07759e0b31463c6b0332a25649450d43d2d11578a3968c0ceee093d0ce"],"state_sha256":"dabca1d29dae0dffc4a6b5f079731317f1dc2412752775928c8abf3b3cff65b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BRG0Q6rPKymsjNsjSz9OzmjoVVCJMF2VYc4SAlkUIebhYGpK0X9ykaAOyMccDsf43jlwV1XdT12G/E5uBoZgDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:22:38.874944Z","bundle_sha256":"a87b3656b34f14c8923e0f65cd3c7637ce25994727b87d4dec410e908ebca3d8"}}