{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:J5VVDNHFSBCTXCNR3FRMEDG5TG","short_pith_number":"pith:J5VVDNHF","schema_version":"1.0","canonical_sha256":"4f6b51b4e590453b89b1d962c20cdd99b543b4fe6f23738eaf59ac962fed5f05","source":{"kind":"arxiv","id":"1609.09365","version":3},"attestation_state":"computed","paper":{"title":"Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Dominic Wang, Dushyant Rao, Ingmar Posner, Julie Dequaire, Peter Ondruska","submitted_at":"2016-09-29T14:39:10Z","abstract_excerpt":"This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approaches to tracking, this method is learned end-to-end, and is able to directly predict a full unoccluded occupancy grid map from raw laser input data. Inspired by the recently presented DeepTracking approach [Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the temporal evolution of the state of the environment, and propose to use Spatial Transformer modules to exploit estimates of the egomotion of "},"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":"1609.09365","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-29T14:39:10Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"5f7d7c50acaec4b19f492862e37a53e4d5af9122b688e5b906d55b90b12cf79c","abstract_canon_sha256":"4a342e01a118cb396261e856b07526cc06c2ea68204e5dddcf49776e4ac6e788"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:07.966791Z","signature_b64":"d/wja3HucJYpuDZtij2dHBbhsd8vn7Ih2PFA/TyPG5bLzkzDruCPMWbHQj+Pevow2syLAzgli5XMTEsCxqGeBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f6b51b4e590453b89b1d962c20cdd99b543b4fe6f23738eaf59ac962fed5f05","last_reissued_at":"2026-05-18T00:46:07.966310Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:07.966310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Dominic Wang, Dushyant Rao, Ingmar Posner, Julie Dequaire, Peter Ondruska","submitted_at":"2016-09-29T14:39:10Z","abstract_excerpt":"This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approaches to tracking, this method is learned end-to-end, and is able to directly predict a full unoccluded occupancy grid map from raw laser input data. Inspired by the recently presented DeepTracking approach [Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the temporal evolution of the state of the environment, and propose to use Spatial Transformer modules to exploit estimates of the egomotion of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.09365","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1609.09365","created_at":"2026-05-18T00:46:07.966398+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.09365v3","created_at":"2026-05-18T00:46:07.966398+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.09365","created_at":"2026-05-18T00:46:07.966398+00:00"},{"alias_kind":"pith_short_12","alias_value":"J5VVDNHFSBCT","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_16","alias_value":"J5VVDNHFSBCTXCNR","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_8","alias_value":"J5VVDNHF","created_at":"2026-05-18T12:30:22.444734+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/J5VVDNHFSBCTXCNR3FRMEDG5TG","json":"https://pith.science/pith/J5VVDNHFSBCTXCNR3FRMEDG5TG.json","graph_json":"https://pith.science/api/pith-number/J5VVDNHFSBCTXCNR3FRMEDG5TG/graph.json","events_json":"https://pith.science/api/pith-number/J5VVDNHFSBCTXCNR3FRMEDG5TG/events.json","paper":"https://pith.science/paper/J5VVDNHF"},"agent_actions":{"view_html":"https://pith.science/pith/J5VVDNHFSBCTXCNR3FRMEDG5TG","download_json":"https://pith.science/pith/J5VVDNHFSBCTXCNR3FRMEDG5TG.json","view_paper":"https://pith.science/paper/J5VVDNHF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.09365&json=true","fetch_graph":"https://pith.science/api/pith-number/J5VVDNHFSBCTXCNR3FRMEDG5TG/graph.json","fetch_events":"https://pith.science/api/pith-number/J5VVDNHFSBCTXCNR3FRMEDG5TG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J5VVDNHFSBCTXCNR3FRMEDG5TG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J5VVDNHFSBCTXCNR3FRMEDG5TG/action/storage_attestation","attest_author":"https://pith.science/pith/J5VVDNHFSBCTXCNR3FRMEDG5TG/action/author_attestation","sign_citation":"https://pith.science/pith/J5VVDNHFSBCTXCNR3FRMEDG5TG/action/citation_signature","submit_replication":"https://pith.science/pith/J5VVDNHFSBCTXCNR3FRMEDG5TG/action/replication_record"}},"created_at":"2026-05-18T00:46:07.966398+00:00","updated_at":"2026-05-18T00:46:07.966398+00:00"}