{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:YTDA5DLL7SAQTN37CCKOAA4X4L","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"214cbe683d5cbd2c3be8ad05ebfe3c1f00ab05b2b058cdc8cc5f03e6cc5de5bf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-11-01T14:18:45Z","title_canon_sha256":"7922a6d0d94d6ff547af4777ef9e97d6dee13fae6748ac66193bf5133e4554e7"},"schema_version":"1.0","source":{"id":"2011.00509","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.00509","created_at":"2026-07-05T03:01:53Z"},{"alias_kind":"arxiv_version","alias_value":"2011.00509v3","created_at":"2026-07-05T03:01:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.00509","created_at":"2026-07-05T03:01:53Z"},{"alias_kind":"pith_short_12","alias_value":"YTDA5DLL7SAQ","created_at":"2026-07-05T03:01:53Z"},{"alias_kind":"pith_short_16","alias_value":"YTDA5DLL7SAQTN37","created_at":"2026-07-05T03:01:53Z"},{"alias_kind":"pith_short_8","alias_value":"YTDA5DLL","created_at":"2026-07-05T03:01:53Z"}],"graph_snapshots":[{"event_id":"sha256:26eaf90b6b82608c9bfe53cccb5b1a003dc72ecb7faf4c960d93fc9af67345fb","target":"graph","created_at":"2026-07-05T03:01:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2011.00509/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Achieving a proper balance between planning quality, safety and efficiency is a major challenge for autonomous driving. Optimisation-based motion planners are capable of producing safe, smooth and comfortable plans, but often at the cost of runtime efficiency. On the other hand, naively deploying trajectories produced by efficient-to-run deep imitation learning approaches might risk compromising safety. In this paper, we present PILOT -- a planning framework that comprises an imitation neural network followed by an efficient optimiser that actively rectifies the network's plan, guaranteeing fu","authors_text":"Francisco Eiras, Henry Pulver, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-11-01T14:18:45Z","title":"PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.00509","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b9a3f7573d6223e9ebfd5e651e3f98999f328a3c10d66ff4166c42bf3162a00a","target":"record","created_at":"2026-07-05T03:01:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"214cbe683d5cbd2c3be8ad05ebfe3c1f00ab05b2b058cdc8cc5f03e6cc5de5bf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-11-01T14:18:45Z","title_canon_sha256":"7922a6d0d94d6ff547af4777ef9e97d6dee13fae6748ac66193bf5133e4554e7"},"schema_version":"1.0","source":{"id":"2011.00509","kind":"arxiv","version":3}},"canonical_sha256":"c4c60e8d6bfc8109b77f1094e00397e2fcef469e2251e05f55152bb6d83abe7f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c4c60e8d6bfc8109b77f1094e00397e2fcef469e2251e05f55152bb6d83abe7f","first_computed_at":"2026-07-05T03:01:53.574603Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:01:53.574603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"opyBVCIJ8O93KPdHxb1WR0Zk7idYH0ZaavkUCSgwLMwSoxTejdNPmBTVob8NL0eN7YuNR4Si3Yyyu6B/4fNYBw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:01:53.575036Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.00509","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9a3f7573d6223e9ebfd5e651e3f98999f328a3c10d66ff4166c42bf3162a00a","sha256:26eaf90b6b82608c9bfe53cccb5b1a003dc72ecb7faf4c960d93fc9af67345fb"],"state_sha256":"0e247954ac017bf942eb525a57bb931005d2d6a1291454da15866248eb598dac"}