{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HVOQXT7IUPVUJDTBCQWCTE5FIJ","short_pith_number":"pith:HVOQXT7I","schema_version":"1.0","canonical_sha256":"3d5d0bcfe8a3eb448e61142c2993a5424edd54478f49329f9f826e6e04d03c7e","source":{"kind":"arxiv","id":"2310.17381","version":2},"attestation_state":"computed","paper":{"title":"Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Azita Dabiri, Bart De Schutter, Jelske Verkuijlen, Leila Gharavi, Simone Baldi","submitted_at":"2023-10-26T13:25:30Z","abstract_excerpt":"Uncertainty in the behavior of other traffic participants is a crucial factor in collision avoidance for automated driving; here, stochastic metrics could avoid overly conservative decisions. This paper introduces a Stochastic Model Predictive Control (SMPC) planner for emergency collision avoidance in highway scenarios to proactively minimize collision risk while ensuring safety through chance constraints. To guarantee that the emergency trajectory can be attained, we incorporate nonlinear tire dynamics in the prediction model of the ego vehicle. Further, we exploit Max-Min-Plus-Scaling (MMPS"},"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":"2310.17381","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.SY","submitted_at":"2023-10-26T13:25:30Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"28ff006c85d68be350c2cfb0e2d018bcdb0c5e8f60e6ef2bd24f295723ecb030","abstract_canon_sha256":"226051d11d017b36bb6682fa87a8bf35ab82c9fb1664002e9a4f126051c4cdce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:19:24.756987Z","signature_b64":"SbrbY4S0PsD4dqJmj1s2QlXIwhWelFdGjlmuvsHRSvMtkhC94BAcBtvMhYa1hK9Z6gMWUHR4cwZuiPiPKOkvDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d5d0bcfe8a3eb448e61142c2993a5424edd54478f49329f9f826e6e04d03c7e","last_reissued_at":"2026-07-05T09:19:24.756484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:19:24.756484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Azita Dabiri, Bart De Schutter, Jelske Verkuijlen, Leila Gharavi, Simone Baldi","submitted_at":"2023-10-26T13:25:30Z","abstract_excerpt":"Uncertainty in the behavior of other traffic participants is a crucial factor in collision avoidance for automated driving; here, stochastic metrics could avoid overly conservative decisions. This paper introduces a Stochastic Model Predictive Control (SMPC) planner for emergency collision avoidance in highway scenarios to proactively minimize collision risk while ensuring safety through chance constraints. To guarantee that the emergency trajectory can be attained, we incorporate nonlinear tire dynamics in the prediction model of the ego vehicle. Further, we exploit Max-Min-Plus-Scaling (MMPS"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.17381","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/2310.17381/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":"2310.17381","created_at":"2026-07-05T09:19:24.756549+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.17381v2","created_at":"2026-07-05T09:19:24.756549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.17381","created_at":"2026-07-05T09:19:24.756549+00:00"},{"alias_kind":"pith_short_12","alias_value":"HVOQXT7IUPVU","created_at":"2026-07-05T09:19:24.756549+00:00"},{"alias_kind":"pith_short_16","alias_value":"HVOQXT7IUPVUJDTB","created_at":"2026-07-05T09:19:24.756549+00:00"},{"alias_kind":"pith_short_8","alias_value":"HVOQXT7I","created_at":"2026-07-05T09:19:24.756549+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/HVOQXT7IUPVUJDTBCQWCTE5FIJ","json":"https://pith.science/pith/HVOQXT7IUPVUJDTBCQWCTE5FIJ.json","graph_json":"https://pith.science/api/pith-number/HVOQXT7IUPVUJDTBCQWCTE5FIJ/graph.json","events_json":"https://pith.science/api/pith-number/HVOQXT7IUPVUJDTBCQWCTE5FIJ/events.json","paper":"https://pith.science/paper/HVOQXT7I"},"agent_actions":{"view_html":"https://pith.science/pith/HVOQXT7IUPVUJDTBCQWCTE5FIJ","download_json":"https://pith.science/pith/HVOQXT7IUPVUJDTBCQWCTE5FIJ.json","view_paper":"https://pith.science/paper/HVOQXT7I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.17381&json=true","fetch_graph":"https://pith.science/api/pith-number/HVOQXT7IUPVUJDTBCQWCTE5FIJ/graph.json","fetch_events":"https://pith.science/api/pith-number/HVOQXT7IUPVUJDTBCQWCTE5FIJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HVOQXT7IUPVUJDTBCQWCTE5FIJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HVOQXT7IUPVUJDTBCQWCTE5FIJ/action/storage_attestation","attest_author":"https://pith.science/pith/HVOQXT7IUPVUJDTBCQWCTE5FIJ/action/author_attestation","sign_citation":"https://pith.science/pith/HVOQXT7IUPVUJDTBCQWCTE5FIJ/action/citation_signature","submit_replication":"https://pith.science/pith/HVOQXT7IUPVUJDTBCQWCTE5FIJ/action/replication_record"}},"created_at":"2026-07-05T09:19:24.756549+00:00","updated_at":"2026-07-05T09:19:24.756549+00:00"}