{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IFJUCGV7UG3PJ6B36YQJA4XVTP","short_pith_number":"pith:IFJUCGV7","schema_version":"1.0","canonical_sha256":"4153411abfa1b6f4f83bf6209072f59bfb6295f61319c95c92853ce20c8863a7","source":{"kind":"arxiv","id":"1809.10732","version":2},"attestation_state":"computed","paper":{"title":"Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"cs.RO","authors_text":"Fang-Chieh Chou, Henggang Cui, Jeff Schneider, Nemanja Djuric, Thi Nguyen, Tsung-han Lin, Tzu-Kuo Huang, Vladan Radosavljevic","submitted_at":"2018-09-18T04:07:13Z","abstract_excerpt":"Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there still remains more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for"},"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":"1809.10732","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-09-18T04:07:13Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"65c937e6abcb177201933f8e3d999dc32ccc4e8147d9e07b12ae0cab33155a9e","abstract_canon_sha256":"41c137cb94aed16b57a6082aab0cfbed0f1d5a0e30948a4680bb17081132e6cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:24.076924Z","signature_b64":"jP0XFTNw7sSirkeA27MGdHhZK980Ud+S1zufsB6lfSmKz2X8hODp4xW5kxbcinWSoQKY20Q7sSSUJbiy9XUdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4153411abfa1b6f4f83bf6209072f59bfb6295f61319c95c92853ce20c8863a7","last_reissued_at":"2026-05-17T23:52:24.076356Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:24.076356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"cs.RO","authors_text":"Fang-Chieh Chou, Henggang Cui, Jeff Schneider, Nemanja Djuric, Thi Nguyen, Tsung-han Lin, Tzu-Kuo Huang, Vladan Radosavljevic","submitted_at":"2018-09-18T04:07:13Z","abstract_excerpt":"Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there still remains more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.10732","kind":"arxiv","version":2},"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":"1809.10732","created_at":"2026-05-17T23:52:24.076458+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.10732v2","created_at":"2026-05-17T23:52:24.076458+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.10732","created_at":"2026-05-17T23:52:24.076458+00:00"},{"alias_kind":"pith_short_12","alias_value":"IFJUCGV7UG3P","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"IFJUCGV7UG3PJ6B3","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"IFJUCGV7","created_at":"2026-05-18T12:32:28.185984+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.10178","citing_title":"Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP","json":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP.json","graph_json":"https://pith.science/api/pith-number/IFJUCGV7UG3PJ6B36YQJA4XVTP/graph.json","events_json":"https://pith.science/api/pith-number/IFJUCGV7UG3PJ6B36YQJA4XVTP/events.json","paper":"https://pith.science/paper/IFJUCGV7"},"agent_actions":{"view_html":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP","download_json":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP.json","view_paper":"https://pith.science/paper/IFJUCGV7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.10732&json=true","fetch_graph":"https://pith.science/api/pith-number/IFJUCGV7UG3PJ6B36YQJA4XVTP/graph.json","fetch_events":"https://pith.science/api/pith-number/IFJUCGV7UG3PJ6B36YQJA4XVTP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP/action/storage_attestation","attest_author":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP/action/author_attestation","sign_citation":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP/action/citation_signature","submit_replication":"https://pith.science/pith/IFJUCGV7UG3PJ6B36YQJA4XVTP/action/replication_record"}},"created_at":"2026-05-17T23:52:24.076458+00:00","updated_at":"2026-05-17T23:52:24.076458+00:00"}