{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3ZLMRLV4BGKA2KNAPZLT5JTXCL","short_pith_number":"pith:3ZLMRLV4","canonical_record":{"source":{"id":"1904.02765","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-04-04T19:43:14Z","cross_cats_sorted":["cs.LG","cs.SY","math.OC"],"title_canon_sha256":"60fd231ee07c6e2965c54cbc1eb125d910c019915f0296c6409132f5dd9a2bee","abstract_canon_sha256":"9c43bdf97bd8d886694d372923b4a01168efc46680c23c0fae5fdb8646950323"},"schema_version":"1.0"},"canonical_sha256":"de56c8aebc09940d29a07e573ea67712d801d433b2a09d5723be197667805103","source":{"kind":"arxiv","id":"1904.02765","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.02765","created_at":"2026-05-17T23:49:19Z"},{"alias_kind":"arxiv_version","alias_value":"1904.02765v1","created_at":"2026-05-17T23:49:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.02765","created_at":"2026-05-17T23:49:19Z"},{"alias_kind":"pith_short_12","alias_value":"3ZLMRLV4BGKA","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"3ZLMRLV4BGKA2KNA","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"3ZLMRLV4","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3ZLMRLV4BGKA2KNAPZLT5JTXCL","target":"record","payload":{"canonical_record":{"source":{"id":"1904.02765","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-04-04T19:43:14Z","cross_cats_sorted":["cs.LG","cs.SY","math.OC"],"title_canon_sha256":"60fd231ee07c6e2965c54cbc1eb125d910c019915f0296c6409132f5dd9a2bee","abstract_canon_sha256":"9c43bdf97bd8d886694d372923b4a01168efc46680c23c0fae5fdb8646950323"},"schema_version":"1.0"},"canonical_sha256":"de56c8aebc09940d29a07e573ea67712d801d433b2a09d5723be197667805103","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:19.734462Z","signature_b64":"KsMfFY8T6diTYC8ayE00nvq8i2qJVgiVoaqQU5E1VZnpUj+xF++cJjzG5CEBw2w82zJf42NlUv5io9EDUGXVAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de56c8aebc09940d29a07e573ea67712d801d433b2a09d5723be197667805103","last_reissued_at":"2026-05-17T23:49:19.733903Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:19.733903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.02765","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":"2026-05-17T23:49:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PBJwzbjVcQpBGdfbBOvG1TO8DrTZKs+vjNjmKfMnJKb3TBIUXFOJO52PYFnmg6Pk7NxQ2HLDKqhGHdqGt2AUCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T23:10:35.977831Z"},"content_sha256":"85bf5f3bc7f7a79c48aff78cbc31828d416d021f8489ee2539f77e0521470b8b","schema_version":"1.0","event_id":"sha256:85bf5f3bc7f7a79c48aff78cbc31828d416d021f8489ee2539f77e0521470b8b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3ZLMRLV4BGKA2KNAPZLT5JTXCL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY","math.OC"],"primary_cat":"cs.RO","authors_text":"Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan","submitted_at":"2019-04-04T19:43:14Z","abstract_excerpt":"Collision prediction in a dynamic and unknown environment relies on knowledge of how the environment is changing. Many collision prediction methods rely on deterministic knowledge of how obstacles are moving in the environment. However, complete deterministic knowledge of the obstacles' motion is often unavailable. This work proposes a Gaussian process based prediction method that replaces the assumption of deterministic knowledge of each obstacle's future behavior with probabilistic knowledge, to allow a larger class of obstacles to be considered. The method solely relies on position and velo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.02765","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":"2026-05-17T23:49:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8LZ6b3MRiHOa2UBNwDJlICXTGETgkaK8xgHP4W/5aoKVu+Vkmti8u7RE2c/UBzZPPiAFvWTUsnE5F4I+Lg0OCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T23:10:35.978195Z"},"content_sha256":"b5cce16659ee48a90ba2d5e7ce0f97391b3defa74809ca0d9988a6a3e10ba405","schema_version":"1.0","event_id":"sha256:b5cce16659ee48a90ba2d5e7ce0f97391b3defa74809ca0d9988a6a3e10ba405"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3ZLMRLV4BGKA2KNAPZLT5JTXCL/bundle.json","state_url":"https://pith.science/pith/3ZLMRLV4BGKA2KNAPZLT5JTXCL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3ZLMRLV4BGKA2KNAPZLT5JTXCL/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":"2026-05-21T23:10:35Z","links":{"resolver":"https://pith.science/pith/3ZLMRLV4BGKA2KNAPZLT5JTXCL","bundle":"https://pith.science/pith/3ZLMRLV4BGKA2KNAPZLT5JTXCL/bundle.json","state":"https://pith.science/pith/3ZLMRLV4BGKA2KNAPZLT5JTXCL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3ZLMRLV4BGKA2KNAPZLT5JTXCL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3ZLMRLV4BGKA2KNAPZLT5JTXCL","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":"9c43bdf97bd8d886694d372923b4a01168efc46680c23c0fae5fdb8646950323","cross_cats_sorted":["cs.LG","cs.SY","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-04-04T19:43:14Z","title_canon_sha256":"60fd231ee07c6e2965c54cbc1eb125d910c019915f0296c6409132f5dd9a2bee"},"schema_version":"1.0","source":{"id":"1904.02765","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.02765","created_at":"2026-05-17T23:49:19Z"},{"alias_kind":"arxiv_version","alias_value":"1904.02765v1","created_at":"2026-05-17T23:49:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.02765","created_at":"2026-05-17T23:49:19Z"},{"alias_kind":"pith_short_12","alias_value":"3ZLMRLV4BGKA","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"3ZLMRLV4BGKA2KNA","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"3ZLMRLV4","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:b5cce16659ee48a90ba2d5e7ce0f97391b3defa74809ca0d9988a6a3e10ba405","target":"graph","created_at":"2026-05-17T23:49:19Z","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":"Collision prediction in a dynamic and unknown environment relies on knowledge of how the environment is changing. Many collision prediction methods rely on deterministic knowledge of how obstacles are moving in the environment. However, complete deterministic knowledge of the obstacles' motion is often unavailable. This work proposes a Gaussian process based prediction method that replaces the assumption of deterministic knowledge of each obstacle's future behavior with probabilistic knowledge, to allow a larger class of obstacles to be considered. The method solely relies on position and velo","authors_text":"Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan","cross_cats":["cs.LG","cs.SY","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-04-04T19:43:14Z","title":"Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.02765","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":"sha256:85bf5f3bc7f7a79c48aff78cbc31828d416d021f8489ee2539f77e0521470b8b","target":"record","created_at":"2026-05-17T23:49:19Z","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":"9c43bdf97bd8d886694d372923b4a01168efc46680c23c0fae5fdb8646950323","cross_cats_sorted":["cs.LG","cs.SY","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-04-04T19:43:14Z","title_canon_sha256":"60fd231ee07c6e2965c54cbc1eb125d910c019915f0296c6409132f5dd9a2bee"},"schema_version":"1.0","source":{"id":"1904.02765","kind":"arxiv","version":1}},"canonical_sha256":"de56c8aebc09940d29a07e573ea67712d801d433b2a09d5723be197667805103","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de56c8aebc09940d29a07e573ea67712d801d433b2a09d5723be197667805103","first_computed_at":"2026-05-17T23:49:19.733903Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:19.733903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KsMfFY8T6diTYC8ayE00nvq8i2qJVgiVoaqQU5E1VZnpUj+xF++cJjzG5CEBw2w82zJf42NlUv5io9EDUGXVAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:19.734462Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.02765","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85bf5f3bc7f7a79c48aff78cbc31828d416d021f8489ee2539f77e0521470b8b","sha256:b5cce16659ee48a90ba2d5e7ce0f97391b3defa74809ca0d9988a6a3e10ba405"],"state_sha256":"10159a7c65da874452a06d4bc717b80bcebe991fab9c7c8eee1a4535160fc0eb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bb0jdcFDoKnUkD/0uJqdprFmTuWq4RIaKNyfLEFQXIyt1YbYdq1bdQ9Gueh4sY5zM4OjiypOLTxxSHZm/thGDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T23:10:35.980787Z","bundle_sha256":"8d6a15789ce656f26510d5e42be49d82ed2c91b65ceadc123f9e1e27ffc3d9e0"}}