{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:EJP2DXVLVBLZUYNGAYWJRQ6CXZ","short_pith_number":"pith:EJP2DXVL","canonical_record":{"source":{"id":"1903.00847","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-03T06:54:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d14a7720d7e98a119ef662ce8068528364d4b32a518857b3475afb516b2d6edf","abstract_canon_sha256":"4d87a3a46c8c244e96b67f8ed0ba254ab6eaa9ff3118f18bb34af158d4fe3643"},"schema_version":"1.0"},"canonical_sha256":"225fa1deaba8579a61a6062c98c3c2be7d32dbe6bb73329ce4755ead5ae2e5bb","source":{"kind":"arxiv","id":"1903.00847","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00847","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00847v1","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00847","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"pith_short_12","alias_value":"EJP2DXVLVBLZ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"EJP2DXVLVBLZUYNG","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"EJP2DXVL","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:EJP2DXVLVBLZUYNGAYWJRQ6CXZ","target":"record","payload":{"canonical_record":{"source":{"id":"1903.00847","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-03T06:54:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d14a7720d7e98a119ef662ce8068528364d4b32a518857b3475afb516b2d6edf","abstract_canon_sha256":"4d87a3a46c8c244e96b67f8ed0ba254ab6eaa9ff3118f18bb34af158d4fe3643"},"schema_version":"1.0"},"canonical_sha256":"225fa1deaba8579a61a6062c98c3c2be7d32dbe6bb73329ce4755ead5ae2e5bb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:13.100350Z","signature_b64":"8XaRHHoFC/239TpbOMUBTZBXZLPiihizvHFWkIad6E+60AXCHLXl1qcrIseYMxlwM2Nc0tggO58jb9ShdRbMDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"225fa1deaba8579a61a6062c98c3c2be7d32dbe6bb73329ce4755ead5ae2e5bb","last_reissued_at":"2026-05-17T23:52:13.099541Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:13.099541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.00847","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":"2026-05-17T23:52:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2KWmzQJwoVbhx30r9CJ/aulSb1wtiY1/nlF2WCCCvtBGmJrXdrwI7jolQ1F0wc/QI7pCXSSNH7RNh2fcw0W+CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T20:02:41.101546Z"},"content_sha256":"7c16ab37fccffe247afbb0423aade14625641e54971cf4f6df5a24806cbf7d46","schema_version":"1.0","event_id":"sha256:7c16ab37fccffe247afbb0423aade14625641e54971cf4f6df5a24806cbf7d46"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:EJP2DXVLVBLZUYNGAYWJRQ6CXZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Shaojie Shen, Wenchao Ding","submitted_at":"2019-03-03T06:54:20Z","abstract_excerpt":"In this paper, we present an online two-level vehicle trajectory prediction framework for urban autonomous driving where there are complex contextual factors, such as lane geometries, road constructions, traffic regulations and moving agents. Our method combines high-level policy anticipation with low-level context reasoning. We leverage a long short-term memory (LSTM) network to anticipate the vehicle's driving policy (e.g., forward, yield, turn left, turn right, etc.) using its sequential history observations. The policy is then used to guide a low-level optimization-based context reasoning "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00847","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:52:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PXLUjQOyTANjd5IQdcxhiHsQ/W/WEki0cTNkpWd51CczoGY4VQ4euZV9TrCd5jNt3v80f+YSeaoeOg8SZaCgCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T20:02:41.102206Z"},"content_sha256":"5d1ca8bdab041412a4cc3fc2078033a7ca58dc8c71f4f58fceb79e626bc9e90f","schema_version":"1.0","event_id":"sha256:5d1ca8bdab041412a4cc3fc2078033a7ca58dc8c71f4f58fceb79e626bc9e90f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EJP2DXVLVBLZUYNGAYWJRQ6CXZ/bundle.json","state_url":"https://pith.science/pith/EJP2DXVLVBLZUYNGAYWJRQ6CXZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EJP2DXVLVBLZUYNGAYWJRQ6CXZ/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":"2026-06-08T20:02:41Z","links":{"resolver":"https://pith.science/pith/EJP2DXVLVBLZUYNGAYWJRQ6CXZ","bundle":"https://pith.science/pith/EJP2DXVLVBLZUYNGAYWJRQ6CXZ/bundle.json","state":"https://pith.science/pith/EJP2DXVLVBLZUYNGAYWJRQ6CXZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EJP2DXVLVBLZUYNGAYWJRQ6CXZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:EJP2DXVLVBLZUYNGAYWJRQ6CXZ","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":"4d87a3a46c8c244e96b67f8ed0ba254ab6eaa9ff3118f18bb34af158d4fe3643","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-03T06:54:20Z","title_canon_sha256":"d14a7720d7e98a119ef662ce8068528364d4b32a518857b3475afb516b2d6edf"},"schema_version":"1.0","source":{"id":"1903.00847","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00847","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00847v1","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00847","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"pith_short_12","alias_value":"EJP2DXVLVBLZ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"EJP2DXVLVBLZUYNG","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"EJP2DXVL","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:5d1ca8bdab041412a4cc3fc2078033a7ca58dc8c71f4f58fceb79e626bc9e90f","target":"graph","created_at":"2026-05-17T23:52:13Z","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"},"paper":{"abstract_excerpt":"In this paper, we present an online two-level vehicle trajectory prediction framework for urban autonomous driving where there are complex contextual factors, such as lane geometries, road constructions, traffic regulations and moving agents. Our method combines high-level policy anticipation with low-level context reasoning. We leverage a long short-term memory (LSTM) network to anticipate the vehicle's driving policy (e.g., forward, yield, turn left, turn right, etc.) using its sequential history observations. The policy is then used to guide a low-level optimization-based context reasoning ","authors_text":"Shaojie Shen, Wenchao Ding","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-03T06:54:20Z","title":"Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00847","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":"sha256:7c16ab37fccffe247afbb0423aade14625641e54971cf4f6df5a24806cbf7d46","target":"record","created_at":"2026-05-17T23:52:13Z","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":"4d87a3a46c8c244e96b67f8ed0ba254ab6eaa9ff3118f18bb34af158d4fe3643","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-03-03T06:54:20Z","title_canon_sha256":"d14a7720d7e98a119ef662ce8068528364d4b32a518857b3475afb516b2d6edf"},"schema_version":"1.0","source":{"id":"1903.00847","kind":"arxiv","version":1}},"canonical_sha256":"225fa1deaba8579a61a6062c98c3c2be7d32dbe6bb73329ce4755ead5ae2e5bb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"225fa1deaba8579a61a6062c98c3c2be7d32dbe6bb73329ce4755ead5ae2e5bb","first_computed_at":"2026-05-17T23:52:13.099541Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:13.099541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8XaRHHoFC/239TpbOMUBTZBXZLPiihizvHFWkIad6E+60AXCHLXl1qcrIseYMxlwM2Nc0tggO58jb9ShdRbMDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:13.100350Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.00847","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c16ab37fccffe247afbb0423aade14625641e54971cf4f6df5a24806cbf7d46","sha256:5d1ca8bdab041412a4cc3fc2078033a7ca58dc8c71f4f58fceb79e626bc9e90f"],"state_sha256":"00fb77356f3b5dd88e04920d016f8bec895fccbd4aea082d87461c64a9a8b848"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7kD8DTtBwkf9zpS3BjrgYNGJ2jHjs1dqK6zctYxlRDBGuw35XF0b4vKTJY+EMbSmauJ4eBdwYG2ESsoTFf2eAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T20:02:41.106034Z","bundle_sha256":"d6e17c0112edfe8b799f6282d732c4155e9175ecd5b733ec4ac4639ce6a0d3f0"}}