{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:HQYR6IJYBBRBOKHZ3W3J76XAZ2","short_pith_number":"pith:HQYR6IJY","schema_version":"1.0","canonical_sha256":"3c311f213808621728f9ddb69ffae0cea6a0b3be400cab80e24fa8c9236852df","source":{"kind":"arxiv","id":"2605.28552","version":1},"attestation_state":"computed","paper":{"title":"Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Di Yang, Hong Yang, Junqing Wang, Kun Xie, Qingwen Pu","submitted_at":"2026-05-27T14:44:10Z","abstract_excerpt":"As automated vehicles (AVs) increasingly share roadways with human-driven vehicles (HDVs), understanding how pedestrians respond to different vehicle types in safety-critical interactions is essential for the safe deployment of automated driving technologies. This study extracts safety-critical pedestrian-vehicle interactions from the Argoverse 2 dataset to capture real-world crash avoidance behaviors in encounters involving AVs and HDVs. To model vehicle-type-specific pedestrian crash avoidance behavior, we develop a Smooth-Mamba Deep Deterministic Policy Gradient framework, termed SMamba-DDP"},"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":"2605.28552","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-27T14:44:10Z","cross_cats_sorted":[],"title_canon_sha256":"19a03da73c381b88f818f3d656a377c13cf20905d123cae571c9de72286c2a1e","abstract_canon_sha256":"2456779c74d9bdd2c0ef170dbc128ff3bd1658ad1b14a43b6224bb890c8597e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T02:04:56.168283Z","signature_b64":"BWp/5/6qSCynk7G80rv1nCSQEG5ZrHjT/EutQ0EPupGWZMur4hRbaJm4ffcLsq5VFI/O7oY3iljMLmifXDyLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c311f213808621728f9ddb69ffae0cea6a0b3be400cab80e24fa8c9236852df","last_reissued_at":"2026-05-28T02:04:56.167723Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T02:04:56.167723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Di Yang, Hong Yang, Junqing Wang, Kun Xie, Qingwen Pu","submitted_at":"2026-05-27T14:44:10Z","abstract_excerpt":"As automated vehicles (AVs) increasingly share roadways with human-driven vehicles (HDVs), understanding how pedestrians respond to different vehicle types in safety-critical interactions is essential for the safe deployment of automated driving technologies. This study extracts safety-critical pedestrian-vehicle interactions from the Argoverse 2 dataset to capture real-world crash avoidance behaviors in encounters involving AVs and HDVs. To model vehicle-type-specific pedestrian crash avoidance behavior, we develop a Smooth-Mamba Deep Deterministic Policy Gradient framework, termed SMamba-DDP"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28552","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/2605.28552/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":"2605.28552","created_at":"2026-05-28T02:04:56.167795+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28552v1","created_at":"2026-05-28T02:04:56.167795+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28552","created_at":"2026-05-28T02:04:56.167795+00:00"},{"alias_kind":"pith_short_12","alias_value":"HQYR6IJYBBRB","created_at":"2026-05-28T02:04:56.167795+00:00"},{"alias_kind":"pith_short_16","alias_value":"HQYR6IJYBBRBOKHZ","created_at":"2026-05-28T02:04:56.167795+00:00"},{"alias_kind":"pith_short_8","alias_value":"HQYR6IJY","created_at":"2026-05-28T02:04:56.167795+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/HQYR6IJYBBRBOKHZ3W3J76XAZ2","json":"https://pith.science/pith/HQYR6IJYBBRBOKHZ3W3J76XAZ2.json","graph_json":"https://pith.science/api/pith-number/HQYR6IJYBBRBOKHZ3W3J76XAZ2/graph.json","events_json":"https://pith.science/api/pith-number/HQYR6IJYBBRBOKHZ3W3J76XAZ2/events.json","paper":"https://pith.science/paper/HQYR6IJY"},"agent_actions":{"view_html":"https://pith.science/pith/HQYR6IJYBBRBOKHZ3W3J76XAZ2","download_json":"https://pith.science/pith/HQYR6IJYBBRBOKHZ3W3J76XAZ2.json","view_paper":"https://pith.science/paper/HQYR6IJY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28552&json=true","fetch_graph":"https://pith.science/api/pith-number/HQYR6IJYBBRBOKHZ3W3J76XAZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/HQYR6IJYBBRBOKHZ3W3J76XAZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HQYR6IJYBBRBOKHZ3W3J76XAZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HQYR6IJYBBRBOKHZ3W3J76XAZ2/action/storage_attestation","attest_author":"https://pith.science/pith/HQYR6IJYBBRBOKHZ3W3J76XAZ2/action/author_attestation","sign_citation":"https://pith.science/pith/HQYR6IJYBBRBOKHZ3W3J76XAZ2/action/citation_signature","submit_replication":"https://pith.science/pith/HQYR6IJYBBRBOKHZ3W3J76XAZ2/action/replication_record"}},"created_at":"2026-05-28T02:04:56.167795+00:00","updated_at":"2026-05-28T02:04:56.167795+00:00"}