{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SIEJIZ5DRPZXZVNSFKW2EFJLUI","short_pith_number":"pith:SIEJIZ5D","schema_version":"1.0","canonical_sha256":"92089467a38bf37cd5b22aada2152ba207d159d42fea87d9168580d7f03f29d2","source":{"kind":"arxiv","id":"2408.08242","version":2},"attestation_state":"computed","paper":{"title":"A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Hanyang Zhuang, Jianglin Lan, Qi Zhang, Xianxian Zhao, Zhen Tian, Zhihao Lin, Ziyang Ye","submitted_at":"2024-08-15T16:10:25Z","abstract_excerpt":"Safety and efficiency are crucial for autonomous driving in roundabouts, especially mixed traffic with both autonomous vehicles (AVs) and human-driven vehicles. This paper presents a learning-based algorithm that promotes safe and efficient driving across varying roundabout traffic conditions. A deep Q-learning network is used to learn optimal strategies in complex multi-vehicle roundabout scenarios, while a Kolmogorov-Arnold Network (KAN) improves the AVs' environmental understanding. To further enhance safety, an action inspector filters unsafe actions, and a route planner optimizes driving "},"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":"2408.08242","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-08-15T16:10:25Z","cross_cats_sorted":["cs.AI","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"fb557e457fb9ed66c3ad356801da807ffc4d7049ffd311a579d059bcf0c12302","abstract_canon_sha256":"a931dddda5b08ba6263f08226afca62161bc866e9b45d7ca377d33d7008491e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:10:52.987596Z","signature_b64":"dUJoKW0+jRy+eJEQ6RZfsFGzIZwrZa0Y7J62SZf0I9YzsbQgAimX0KIiOIGJuDSeZRKOf6lgKaDnJvjS2i3BCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92089467a38bf37cd5b22aada2152ba207d159d42fea87d9168580d7f03f29d2","last_reissued_at":"2026-07-05T12:10:52.987096Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:10:52.987096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Hanyang Zhuang, Jianglin Lan, Qi Zhang, Xianxian Zhao, Zhen Tian, Zhihao Lin, Ziyang Ye","submitted_at":"2024-08-15T16:10:25Z","abstract_excerpt":"Safety and efficiency are crucial for autonomous driving in roundabouts, especially mixed traffic with both autonomous vehicles (AVs) and human-driven vehicles. This paper presents a learning-based algorithm that promotes safe and efficient driving across varying roundabout traffic conditions. A deep Q-learning network is used to learn optimal strategies in complex multi-vehicle roundabout scenarios, while a Kolmogorov-Arnold Network (KAN) improves the AVs' environmental understanding. To further enhance safety, an action inspector filters unsafe actions, and a route planner optimizes driving "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08242","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/2408.08242/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":"2408.08242","created_at":"2026-07-05T12:10:52.987156+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.08242v2","created_at":"2026-07-05T12:10:52.987156+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08242","created_at":"2026-07-05T12:10:52.987156+00:00"},{"alias_kind":"pith_short_12","alias_value":"SIEJIZ5DRPZX","created_at":"2026-07-05T12:10:52.987156+00:00"},{"alias_kind":"pith_short_16","alias_value":"SIEJIZ5DRPZXZVNS","created_at":"2026-07-05T12:10:52.987156+00:00"},{"alias_kind":"pith_short_8","alias_value":"SIEJIZ5D","created_at":"2026-07-05T12:10:52.987156+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00981","citing_title":"Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI","json":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI.json","graph_json":"https://pith.science/api/pith-number/SIEJIZ5DRPZXZVNSFKW2EFJLUI/graph.json","events_json":"https://pith.science/api/pith-number/SIEJIZ5DRPZXZVNSFKW2EFJLUI/events.json","paper":"https://pith.science/paper/SIEJIZ5D"},"agent_actions":{"view_html":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI","download_json":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI.json","view_paper":"https://pith.science/paper/SIEJIZ5D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.08242&json=true","fetch_graph":"https://pith.science/api/pith-number/SIEJIZ5DRPZXZVNSFKW2EFJLUI/graph.json","fetch_events":"https://pith.science/api/pith-number/SIEJIZ5DRPZXZVNSFKW2EFJLUI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI/action/storage_attestation","attest_author":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI/action/author_attestation","sign_citation":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI/action/citation_signature","submit_replication":"https://pith.science/pith/SIEJIZ5DRPZXZVNSFKW2EFJLUI/action/replication_record"}},"created_at":"2026-07-05T12:10:52.987156+00:00","updated_at":"2026-07-05T12:10:52.987156+00:00"}