{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:Y5ELEWSV4YMSX3YRIOPNEAC2PV","short_pith_number":"pith:Y5ELEWSV","schema_version":"1.0","canonical_sha256":"c748b25a55e6192bef11439ed2005a7d5fa3b9a8ad5b5a38be7b49e4f6aec2ef","source":{"kind":"arxiv","id":"1611.07608","version":1},"attestation_state":"computed","paper":{"title":"A Framework for Collision-Tolerant Optimal Trajectory Planning of Autonomous Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Eric Feron, Juan-Pablo Afman, Mark L. Mote","submitted_at":"2016-11-23T02:34:15Z","abstract_excerpt":"Collision-tolerant trajectory planning is the consideration that collisions, if they are planned appropriately, enable more effective path planning for robots capable of handling them. A mixed integer programming (MIP) optimization formulation demonstrates the computational practicality of optimizing trajectories that comprise planned collisions. A damage quantification function is proposed, and the influence of damage functions constraints on the trajectory are studied in simulation. Using a simple example, an increase in performance is achieved under this schema as compared to collision-free"},"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":"1611.07608","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2016-11-23T02:34:15Z","cross_cats_sorted":[],"title_canon_sha256":"aefc546104c5c7ecd70ec861aa3f77fe5b6a8314e41db6a88daaab0de7bce572","abstract_canon_sha256":"0f15482cd51f2d9a8ffdb3797e71d1f34d24e4af050cdb3963c578973c3a1b60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:57:00.325399Z","signature_b64":"h6VOWXox16BHgd4dbL85cUZEf2/OrCXCSpS8ed2dWystMIIjK1kSDm5iukTP7Sw8F23K+r8S1chPZb4ic/9zCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c748b25a55e6192bef11439ed2005a7d5fa3b9a8ad5b5a38be7b49e4f6aec2ef","last_reissued_at":"2026-05-18T00:57:00.324776Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:57:00.324776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Framework for Collision-Tolerant Optimal Trajectory Planning of Autonomous Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Eric Feron, Juan-Pablo Afman, Mark L. Mote","submitted_at":"2016-11-23T02:34:15Z","abstract_excerpt":"Collision-tolerant trajectory planning is the consideration that collisions, if they are planned appropriately, enable more effective path planning for robots capable of handling them. A mixed integer programming (MIP) optimization formulation demonstrates the computational practicality of optimizing trajectories that comprise planned collisions. A damage quantification function is proposed, and the influence of damage functions constraints on the trajectory are studied in simulation. Using a simple example, an increase in performance is achieved under this schema as compared to collision-free"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.07608","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":""},"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":"1611.07608","created_at":"2026-05-18T00:57:00.324879+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.07608v1","created_at":"2026-05-18T00:57:00.324879+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.07608","created_at":"2026-05-18T00:57:00.324879+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y5ELEWSV4YMS","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y5ELEWSV4YMSX3YR","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y5ELEWSV","created_at":"2026-05-18T12:30:53.716459+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/Y5ELEWSV4YMSX3YRIOPNEAC2PV","json":"https://pith.science/pith/Y5ELEWSV4YMSX3YRIOPNEAC2PV.json","graph_json":"https://pith.science/api/pith-number/Y5ELEWSV4YMSX3YRIOPNEAC2PV/graph.json","events_json":"https://pith.science/api/pith-number/Y5ELEWSV4YMSX3YRIOPNEAC2PV/events.json","paper":"https://pith.science/paper/Y5ELEWSV"},"agent_actions":{"view_html":"https://pith.science/pith/Y5ELEWSV4YMSX3YRIOPNEAC2PV","download_json":"https://pith.science/pith/Y5ELEWSV4YMSX3YRIOPNEAC2PV.json","view_paper":"https://pith.science/paper/Y5ELEWSV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.07608&json=true","fetch_graph":"https://pith.science/api/pith-number/Y5ELEWSV4YMSX3YRIOPNEAC2PV/graph.json","fetch_events":"https://pith.science/api/pith-number/Y5ELEWSV4YMSX3YRIOPNEAC2PV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y5ELEWSV4YMSX3YRIOPNEAC2PV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y5ELEWSV4YMSX3YRIOPNEAC2PV/action/storage_attestation","attest_author":"https://pith.science/pith/Y5ELEWSV4YMSX3YRIOPNEAC2PV/action/author_attestation","sign_citation":"https://pith.science/pith/Y5ELEWSV4YMSX3YRIOPNEAC2PV/action/citation_signature","submit_replication":"https://pith.science/pith/Y5ELEWSV4YMSX3YRIOPNEAC2PV/action/replication_record"}},"created_at":"2026-05-18T00:57:00.324879+00:00","updated_at":"2026-05-18T00:57:00.324879+00:00"}