{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:W3R5IWFSK3XUDRB7LVJ7TFQ5MK","short_pith_number":"pith:W3R5IWFS","schema_version":"1.0","canonical_sha256":"b6e3d458b256ef41c43f5d53f9961d629eb1d92694a5b51c98cfd0776a8e4aac","source":{"kind":"arxiv","id":"2502.17309","version":1},"attestation_state":"computed","paper":{"title":"Hybrid Human-Machine Perception via Adaptive LiDAR for Advanced Driver Assistance Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.RO","authors_text":"Arkady Zgonnikov, Chen Quan, Federico Scar\\`i, Nitin Jonathan Myers","submitted_at":"2025-02-24T16:44:20Z","abstract_excerpt":"Accurate environmental perception is critical for advanced driver assistance systems (ADAS). Light detection and ranging (LiDAR) systems play a crucial role in ADAS; they can reliably detect obstacles and help ensure traffic safety. Existing research on LiDAR sensing has demonstrated that adapting the LiDAR's resolution and range based on environmental characteristics can improve machine perception. However, current adaptive LiDAR approaches for ADAS have not explored the possibility of combining the perception abilities of the vehicle and the human driver, which can potentially further enhanc"},"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":"2502.17309","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-02-24T16:44:20Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"392e3375c97eb6299a4f4fd5510b33342ee8bda6dd409dc85047605986a1b9e5","abstract_canon_sha256":"87fbab1d3a8b7a4a451f1296698ad820a56e8d34326f365182bcb778d9ce4e80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:08.574810Z","signature_b64":"T/kODcAATaJoH4b8PE9rp/HWBs/Zr7C3Ang7XFoW9JCPE6cL228/VS4j8jbN2kTdIzWBh6HaHxajRbqrm6U9CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6e3d458b256ef41c43f5d53f9961d629eb1d92694a5b51c98cfd0776a8e4aac","last_reissued_at":"2026-07-05T10:19:08.574333Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:08.574333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid Human-Machine Perception via Adaptive LiDAR for Advanced Driver Assistance Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.RO","authors_text":"Arkady Zgonnikov, Chen Quan, Federico Scar\\`i, Nitin Jonathan Myers","submitted_at":"2025-02-24T16:44:20Z","abstract_excerpt":"Accurate environmental perception is critical for advanced driver assistance systems (ADAS). Light detection and ranging (LiDAR) systems play a crucial role in ADAS; they can reliably detect obstacles and help ensure traffic safety. Existing research on LiDAR sensing has demonstrated that adapting the LiDAR's resolution and range based on environmental characteristics can improve machine perception. However, current adaptive LiDAR approaches for ADAS have not explored the possibility of combining the perception abilities of the vehicle and the human driver, which can potentially further enhanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17309","kind":"arxiv","version":1},"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/2502.17309/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":"2502.17309","created_at":"2026-07-05T10:19:08.574384+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.17309v1","created_at":"2026-07-05T10:19:08.574384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17309","created_at":"2026-07-05T10:19:08.574384+00:00"},{"alias_kind":"pith_short_12","alias_value":"W3R5IWFSK3XU","created_at":"2026-07-05T10:19:08.574384+00:00"},{"alias_kind":"pith_short_16","alias_value":"W3R5IWFSK3XUDRB7","created_at":"2026-07-05T10:19:08.574384+00:00"},{"alias_kind":"pith_short_8","alias_value":"W3R5IWFS","created_at":"2026-07-05T10:19:08.574384+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07626","citing_title":"Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK","json":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK.json","graph_json":"https://pith.science/api/pith-number/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/graph.json","events_json":"https://pith.science/api/pith-number/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/events.json","paper":"https://pith.science/paper/W3R5IWFS"},"agent_actions":{"view_html":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK","download_json":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK.json","view_paper":"https://pith.science/paper/W3R5IWFS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.17309&json=true","fetch_graph":"https://pith.science/api/pith-number/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/graph.json","fetch_events":"https://pith.science/api/pith-number/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/action/storage_attestation","attest_author":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/action/author_attestation","sign_citation":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/action/citation_signature","submit_replication":"https://pith.science/pith/W3R5IWFSK3XUDRB7LVJ7TFQ5MK/action/replication_record"}},"created_at":"2026-07-05T10:19:08.574384+00:00","updated_at":"2026-07-05T10:19:08.574384+00:00"}