{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PEKAA4GGPM5DDUMDXKD37EA4N7","short_pith_number":"pith:PEKAA4GG","schema_version":"1.0","canonical_sha256":"79140070c67b3a31d183ba87bf901c6fc51ed301156275d14b9a7dec9baf544f","source":{"kind":"arxiv","id":"1907.07792","version":2},"attestation_state":"computed","paper":{"title":"GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Mooi Choo Chuah, Xiaowen Ying, Xin Li","submitted_at":"2019-07-17T22:10:16Z","abstract_excerpt":"Despite the advancement in the technology of autonomous driving cars, the safety of a self-driving car is still a challenging problem that has not been well studied. Motion prediction is one of the core functions of an autonomous driving car. Previously, we propose a novel scheme called GRIP which is designed to predict trajectories for traffic agents around an autonomous car efficiently. GRIP uses a graph to represent the interactions of close objects, applies several graph convolutional blocks to extract features, and subsequently uses an encoder-decoder long short-term memory (LSTM) model t"},"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":"1907.07792","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-17T22:10:16Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"e5320fb6f31e4d9fc0da80027bd856ecd1c8b99f695eeaa75a23f8570d5bc112","abstract_canon_sha256":"a15c9f7756c6d5bb23b777d75f1069372b9ac0037cdf779ec0ae3613ce169e67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:04:28.420045Z","signature_b64":"JQkGaK0/agv84hkzIanSiebu7nJOSpdyxbsuE87WmCEVrE9IA8SwesSArNu9yr1LSKez1tpa9DSHmq8GQJS+AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79140070c67b3a31d183ba87bf901c6fc51ed301156275d14b9a7dec9baf544f","last_reissued_at":"2026-07-05T01:04:28.419484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:04:28.419484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Mooi Choo Chuah, Xiaowen Ying, Xin Li","submitted_at":"2019-07-17T22:10:16Z","abstract_excerpt":"Despite the advancement in the technology of autonomous driving cars, the safety of a self-driving car is still a challenging problem that has not been well studied. Motion prediction is one of the core functions of an autonomous driving car. Previously, we propose a novel scheme called GRIP which is designed to predict trajectories for traffic agents around an autonomous car efficiently. GRIP uses a graph to represent the interactions of close objects, applies several graph convolutional blocks to extract features, and subsequently uses an encoder-decoder long short-term memory (LSTM) model t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07792","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1907.07792/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1907.07792","created_at":"2026-07-05T01:04:28.419550+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.07792v2","created_at":"2026-07-05T01:04:28.419550+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07792","created_at":"2026-07-05T01:04:28.419550+00:00"},{"alias_kind":"pith_short_12","alias_value":"PEKAA4GGPM5D","created_at":"2026-07-05T01:04:28.419550+00:00"},{"alias_kind":"pith_short_16","alias_value":"PEKAA4GGPM5DDUMD","created_at":"2026-07-05T01:04:28.419550+00:00"},{"alias_kind":"pith_short_8","alias_value":"PEKAA4GG","created_at":"2026-07-05T01:04:28.419550+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30777","citing_title":"Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14855","citing_title":"Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00857","citing_title":"From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01301","citing_title":"From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16783","citing_title":"EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications","ref_index":41,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7","json":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7.json","graph_json":"https://pith.science/api/pith-number/PEKAA4GGPM5DDUMDXKD37EA4N7/graph.json","events_json":"https://pith.science/api/pith-number/PEKAA4GGPM5DDUMDXKD37EA4N7/events.json","paper":"https://pith.science/paper/PEKAA4GG"},"agent_actions":{"view_html":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7","download_json":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7.json","view_paper":"https://pith.science/paper/PEKAA4GG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.07792&json=true","fetch_graph":"https://pith.science/api/pith-number/PEKAA4GGPM5DDUMDXKD37EA4N7/graph.json","fetch_events":"https://pith.science/api/pith-number/PEKAA4GGPM5DDUMDXKD37EA4N7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7/action/storage_attestation","attest_author":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7/action/author_attestation","sign_citation":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7/action/citation_signature","submit_replication":"https://pith.science/pith/PEKAA4GGPM5DDUMDXKD37EA4N7/action/replication_record"}},"created_at":"2026-07-05T01:04:28.419550+00:00","updated_at":"2026-07-05T01:04:28.419550+00:00"}