{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4GL2FSVUYQZLGRV3D3WXF7HQV3","short_pith_number":"pith:4GL2FSVU","schema_version":"1.0","canonical_sha256":"e197a2cab4c432b346bb1eed72fcf0aef1c4084b9ec94cc532cec25572633f03","source":{"kind":"arxiv","id":"2502.01894","version":2},"attestation_state":"computed","paper":{"title":"SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Azim Eskandarian, Goodarz Mehr","submitted_at":"2025-02-04T00:00:06Z","abstract_excerpt":"Bird's-eye view (BEV) perception has garnered significant attention in autonomous driving in recent years, in part because BEV representation facilitates multi-modal sensor fusion. BEV representation enables a variety of perception tasks including BEV segmentation, a concise view of the environment useful for planning a vehicle's trajectory. However, this representation is not fully supported by existing datasets, and creation of new datasets for this purpose can be a time-consuming endeavor. To address this challenge, we introduce SimBEV. SimBEV is a randomized synthetic data generation tool "},"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":"2502.01894","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-04T00:00:06Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"25d032093cbca9d72f1bc8960a5e186e9c9704335e7ad0ecbc1e7064230c61ea","abstract_canon_sha256":"ca9f5662df7eb10abce2cbc2a3935bb18ed6b42507466abe5c7fc6a6c9c4d53a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:39:47.149998Z","signature_b64":"gnv7wmcP6iPKPOQgaWAmYW7bcgGLP218hQhkHx/EYLHkBaLnrLrlAAml02l3Gkqg6d265MMTsPT49EYMIo+GBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e197a2cab4c432b346bb1eed72fcf0aef1c4084b9ec94cc532cec25572633f03","last_reissued_at":"2026-07-05T10:39:47.149519Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:39:47.149519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Azim Eskandarian, Goodarz Mehr","submitted_at":"2025-02-04T00:00:06Z","abstract_excerpt":"Bird's-eye view (BEV) perception has garnered significant attention in autonomous driving in recent years, in part because BEV representation facilitates multi-modal sensor fusion. BEV representation enables a variety of perception tasks including BEV segmentation, a concise view of the environment useful for planning a vehicle's trajectory. However, this representation is not fully supported by existing datasets, and creation of new datasets for this purpose can be a time-consuming endeavor. To address this challenge, we introduce SimBEV. SimBEV is a randomized synthetic data generation tool "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01894","kind":"arxiv","version":2},"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/2502.01894/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":"2502.01894","created_at":"2026-07-05T10:39:47.149575+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01894v2","created_at":"2026-07-05T10:39:47.149575+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01894","created_at":"2026-07-05T10:39:47.149575+00:00"},{"alias_kind":"pith_short_12","alias_value":"4GL2FSVUYQZL","created_at":"2026-07-05T10:39:47.149575+00:00"},{"alias_kind":"pith_short_16","alias_value":"4GL2FSVUYQZLGRV3","created_at":"2026-07-05T10:39:47.149575+00:00"},{"alias_kind":"pith_short_8","alias_value":"4GL2FSVU","created_at":"2026-07-05T10:39:47.149575+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3","json":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3.json","graph_json":"https://pith.science/api/pith-number/4GL2FSVUYQZLGRV3D3WXF7HQV3/graph.json","events_json":"https://pith.science/api/pith-number/4GL2FSVUYQZLGRV3D3WXF7HQV3/events.json","paper":"https://pith.science/paper/4GL2FSVU"},"agent_actions":{"view_html":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3","download_json":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3.json","view_paper":"https://pith.science/paper/4GL2FSVU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01894&json=true","fetch_graph":"https://pith.science/api/pith-number/4GL2FSVUYQZLGRV3D3WXF7HQV3/graph.json","fetch_events":"https://pith.science/api/pith-number/4GL2FSVUYQZLGRV3D3WXF7HQV3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3/action/storage_attestation","attest_author":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3/action/author_attestation","sign_citation":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3/action/citation_signature","submit_replication":"https://pith.science/pith/4GL2FSVUYQZLGRV3D3WXF7HQV3/action/replication_record"}},"created_at":"2026-07-05T10:39:47.149575+00:00","updated_at":"2026-07-05T10:39:47.149575+00:00"}