{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:6OLSRQ6T6F22NB66RJ7NJZP27C","short_pith_number":"pith:6OLSRQ6T","canonical_record":{"source":{"id":"2111.12137","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-11-23T20:14:02Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"dbbe4b06cf8d9841b3978985a87b8c52f2bff9bae2287c54c825b4f802561e05","abstract_canon_sha256":"c43b0de8ac0e479defcc1257796e1805265bdc6a9d27e9f921bb11388fb7486a"},"schema_version":"1.0"},"canonical_sha256":"f39728c3d3f175a687de8a7ed4e5faf8b97fa2dc701dbef0f5de847b596fa6aa","source":{"kind":"arxiv","id":"2111.12137","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.12137","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"arxiv_version","alias_value":"2111.12137v1","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.12137","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_12","alias_value":"6OLSRQ6T6F22","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_16","alias_value":"6OLSRQ6T6F22NB66","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_8","alias_value":"6OLSRQ6T","created_at":"2026-07-05T03:34:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:6OLSRQ6T6F22NB66RJ7NJZP27C","target":"record","payload":{"canonical_record":{"source":{"id":"2111.12137","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-11-23T20:14:02Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"dbbe4b06cf8d9841b3978985a87b8c52f2bff9bae2287c54c825b4f802561e05","abstract_canon_sha256":"c43b0de8ac0e479defcc1257796e1805265bdc6a9d27e9f921bb11388fb7486a"},"schema_version":"1.0"},"canonical_sha256":"f39728c3d3f175a687de8a7ed4e5faf8b97fa2dc701dbef0f5de847b596fa6aa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:34:48.592632Z","signature_b64":"Sd1jF1QKUrfmP/RC1Vce3+7HsnEw+3+Fuchsgpoftv+SZcfvFbi/6IghWklyF21E3Ck1doPfwgt4S001drWDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f39728c3d3f175a687de8a7ed4e5faf8b97fa2dc701dbef0f5de847b596fa6aa","last_reissued_at":"2026-07-05T03:34:48.592221Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:34:48.592221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.12137","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:34:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y0XhDYBfDb+kBmySLjn5SGlut6ODqaTRbKY1KrL33tOOI+jd4yvHUH5EW+kFqbESqx5AHDNlADcddYbB6OiKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T08:23:41.885818Z"},"content_sha256":"62da7db56aa537d984baa0c1bd874e937f3e52790ebc9e70b3f92042a1c2b591","schema_version":"1.0","event_id":"sha256:62da7db56aa537d984baa0c1bd874e937f3e52790ebc9e70b3f92042a1c2b591"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:6OLSRQ6T6F22NB66RJ7NJZP27C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Interactive Driving Policies via Data-driven Simulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Alexander Amini, Daniela Rus, Igor Gilitschenski, Sertac Karaman, Tsun-Hsuan Wang, Wilko Schwarting","submitted_at":"2021-11-23T20:14:02Z","abstract_excerpt":"Data-driven simulators promise high data-efficiency for driving policy learning. When used for modelling interactions, this data-efficiency becomes a bottleneck: Small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We address this challenge by proposing a simulation method that uses in-painted ado vehicles for learning robust driving policies. Thus, our approach can be used to learn policies that involve multi-agent interactions and allows for training via state-of-the-art policy learning methods. We evaluate the approach for learning st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.12137","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/2111.12137/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:34:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0wqen9Vl77gyihDUcqQJ8p6b45etxP/8PYUSuXAo8DLxUS2vDR73InfTsFxu5E8onkpaOkBre6teZ0tCoHLECw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T08:23:41.886662Z"},"content_sha256":"be709aba401585738235e4f48f13f16a4406ad54d2de069cc62c23cf037da1e5","schema_version":"1.0","event_id":"sha256:be709aba401585738235e4f48f13f16a4406ad54d2de069cc62c23cf037da1e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6OLSRQ6T6F22NB66RJ7NJZP27C/bundle.json","state_url":"https://pith.science/pith/6OLSRQ6T6F22NB66RJ7NJZP27C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6OLSRQ6T6F22NB66RJ7NJZP27C/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T08:23:41Z","links":{"resolver":"https://pith.science/pith/6OLSRQ6T6F22NB66RJ7NJZP27C","bundle":"https://pith.science/pith/6OLSRQ6T6F22NB66RJ7NJZP27C/bundle.json","state":"https://pith.science/pith/6OLSRQ6T6F22NB66RJ7NJZP27C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6OLSRQ6T6F22NB66RJ7NJZP27C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:6OLSRQ6T6F22NB66RJ7NJZP27C","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":"c43b0de8ac0e479defcc1257796e1805265bdc6a9d27e9f921bb11388fb7486a","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-11-23T20:14:02Z","title_canon_sha256":"dbbe4b06cf8d9841b3978985a87b8c52f2bff9bae2287c54c825b4f802561e05"},"schema_version":"1.0","source":{"id":"2111.12137","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.12137","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"arxiv_version","alias_value":"2111.12137v1","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.12137","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_12","alias_value":"6OLSRQ6T6F22","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_16","alias_value":"6OLSRQ6T6F22NB66","created_at":"2026-07-05T03:34:48Z"},{"alias_kind":"pith_short_8","alias_value":"6OLSRQ6T","created_at":"2026-07-05T03:34:48Z"}],"graph_snapshots":[{"event_id":"sha256:be709aba401585738235e4f48f13f16a4406ad54d2de069cc62c23cf037da1e5","target":"graph","created_at":"2026-07-05T03:34:48Z","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/2111.12137/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data-driven simulators promise high data-efficiency for driving policy learning. When used for modelling interactions, this data-efficiency becomes a bottleneck: Small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We address this challenge by proposing a simulation method that uses in-painted ado vehicles for learning robust driving policies. Thus, our approach can be used to learn policies that involve multi-agent interactions and allows for training via state-of-the-art policy learning methods. We evaluate the approach for learning st","authors_text":"Alexander Amini, Daniela Rus, Igor Gilitschenski, Sertac Karaman, Tsun-Hsuan Wang, Wilko Schwarting","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-11-23T20:14:02Z","title":"Learning Interactive Driving Policies via Data-driven Simulation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.12137","kind":"arxiv","version":1},"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":"sha256:62da7db56aa537d984baa0c1bd874e937f3e52790ebc9e70b3f92042a1c2b591","target":"record","created_at":"2026-07-05T03:34:48Z","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":"c43b0de8ac0e479defcc1257796e1805265bdc6a9d27e9f921bb11388fb7486a","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-11-23T20:14:02Z","title_canon_sha256":"dbbe4b06cf8d9841b3978985a87b8c52f2bff9bae2287c54c825b4f802561e05"},"schema_version":"1.0","source":{"id":"2111.12137","kind":"arxiv","version":1}},"canonical_sha256":"f39728c3d3f175a687de8a7ed4e5faf8b97fa2dc701dbef0f5de847b596fa6aa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f39728c3d3f175a687de8a7ed4e5faf8b97fa2dc701dbef0f5de847b596fa6aa","first_computed_at":"2026-07-05T03:34:48.592221Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:34:48.592221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Sd1jF1QKUrfmP/RC1Vce3+7HsnEw+3+Fuchsgpoftv+SZcfvFbi/6IghWklyF21E3Ck1doPfwgt4S001drWDCg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:34:48.592632Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.12137","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62da7db56aa537d984baa0c1bd874e937f3e52790ebc9e70b3f92042a1c2b591","sha256:be709aba401585738235e4f48f13f16a4406ad54d2de069cc62c23cf037da1e5"],"state_sha256":"b7176796aa64157511d4e0f8539d46eacf6f7169462a5b9f54c61b484f7ca919"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s9Db09XOQLVd0i6D83woG+NWJPCrPxdFifhh/7SiGnDDCm1BWqR+gZ7NMj9XRsnyxWLQJpLRhfBSeZnr9ov/Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T08:23:41.892819Z","bundle_sha256":"5c77ca6143a518ab8bc32c4ad896fa142d6344aa726d8d362d257f67056bc706"}}