{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:FTXNKP3EOD7NGPL62TRVUPOLEX","short_pith_number":"pith:FTXNKP3E","schema_version":"1.0","canonical_sha256":"2ceed53f6470fed33d7ed4e35a3dcb25d8fc557013e4b60782ef9d97c79bee2e","source":{"kind":"arxiv","id":"1805.06771","version":1},"attestation_state":"computed","paper":{"title":"Convolutional Social Pooling for Vehicle Trajectory Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mohan M. Trivedi, Nachiket Deo","submitted_at":"2018-05-15T00:24:38Z","abstract_excerpt":"Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic context, i.e., the motion and relative spatial configuration of neighboring vehicles. In this paper we propose an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social pooling layers for robustly learning interdependencies in vehicle motion. Additionally, our model outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. We evaluate"},"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":"1805.06771","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-15T00:24:38Z","cross_cats_sorted":[],"title_canon_sha256":"bbd224cb6df44c8b504effee05d73c6b3ff05a63529300c805d40999d9a33535","abstract_canon_sha256":"1bb7b1cf0e59e5f87c0482235be90f44e366ced7181978e199cf8f19b20ed89d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:03.161109Z","signature_b64":"pYuMm3902KEgJcjR2WihIyCAOPLws+S/WH6ZpFZARDPIHOCEwAJCqVVUhfQXky+LffJuq4KZvBhqA8LSnuDRDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ceed53f6470fed33d7ed4e35a3dcb25d8fc557013e4b60782ef9d97c79bee2e","last_reissued_at":"2026-05-18T00:02:03.160492Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:03.160492Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convolutional Social Pooling for Vehicle Trajectory Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mohan M. Trivedi, Nachiket Deo","submitted_at":"2018-05-15T00:24:38Z","abstract_excerpt":"Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic context, i.e., the motion and relative spatial configuration of neighboring vehicles. In this paper we propose an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social pooling layers for robustly learning interdependencies in vehicle motion. Additionally, our model outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. We evaluate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06771","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1805.06771","created_at":"2026-05-18T00:02:03.160587+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.06771v1","created_at":"2026-05-18T00:02:03.160587+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06771","created_at":"2026-05-18T00:02:03.160587+00:00"},{"alias_kind":"pith_short_12","alias_value":"FTXNKP3EOD7N","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"FTXNKP3EOD7NGPL6","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"FTXNKP3E","created_at":"2026-05-18T12:32:25.280505+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"1907.08752","citing_title":"RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"1907.10178","citing_title":"Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"1907.10170","citing_title":"Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX","json":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX.json","graph_json":"https://pith.science/api/pith-number/FTXNKP3EOD7NGPL62TRVUPOLEX/graph.json","events_json":"https://pith.science/api/pith-number/FTXNKP3EOD7NGPL62TRVUPOLEX/events.json","paper":"https://pith.science/paper/FTXNKP3E"},"agent_actions":{"view_html":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX","download_json":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX.json","view_paper":"https://pith.science/paper/FTXNKP3E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.06771&json=true","fetch_graph":"https://pith.science/api/pith-number/FTXNKP3EOD7NGPL62TRVUPOLEX/graph.json","fetch_events":"https://pith.science/api/pith-number/FTXNKP3EOD7NGPL62TRVUPOLEX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX/action/storage_attestation","attest_author":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX/action/author_attestation","sign_citation":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX/action/citation_signature","submit_replication":"https://pith.science/pith/FTXNKP3EOD7NGPL62TRVUPOLEX/action/replication_record"}},"created_at":"2026-05-18T00:02:03.160587+00:00","updated_at":"2026-05-18T00:02:03.160587+00:00"}