{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PTK5Q4UADGQACEVABMJJIX3MRF","short_pith_number":"pith:PTK5Q4UA","schema_version":"1.0","canonical_sha256":"7cd5d8728019a00112a00b12945f6c8954667a17a5bb363a0bcb4cbdc073fe0a","source":{"kind":"arxiv","id":"1902.09820","version":1},"attestation_state":"computed","paper":{"title":"Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alessandro Cattaneo, Carlo Alberto Avizzano, Emanuele Ruffaldi, Michele Tonutti","submitted_at":"2019-02-26T09:32:14Z","abstract_excerpt":"Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to tackle this challenge through domain adaptation, a technique closely related to transfer learning. A proof of concept for the application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to multi-modal time series driving data is presented, in which domain-invariant feat"},"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":"1902.09820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-26T09:32:14Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ba27a94dd07e7f9a4c2922aaf56ce2a6ec664cbb244b021a86ce945d0ac5ea94","abstract_canon_sha256":"cd5b26a5c939dea4913855d1c9c416a32dbaf761ec53e4f9bd78295cbe52a3cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:38.948101Z","signature_b64":"KFgxJRBo5cns7BxPYB8tUw/Y2wq5atEvFVTvgGY7IpJ9HXpyzla0CrRLtpg5Hi+v+QKxL+wOHtPSgUKBs2HsBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cd5d8728019a00112a00b12945f6c8954667a17a5bb363a0bcb4cbdc073fe0a","last_reissued_at":"2026-05-17T23:51:38.947508Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:38.947508Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alessandro Cattaneo, Carlo Alberto Avizzano, Emanuele Ruffaldi, Michele Tonutti","submitted_at":"2019-02-26T09:32:14Z","abstract_excerpt":"Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to tackle this challenge through domain adaptation, a technique closely related to transfer learning. A proof of concept for the application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to multi-modal time series driving data is presented, in which domain-invariant feat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09820","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":"1902.09820","created_at":"2026-05-17T23:51:38.947593+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.09820v1","created_at":"2026-05-17T23:51:38.947593+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09820","created_at":"2026-05-17T23:51:38.947593+00:00"},{"alias_kind":"pith_short_12","alias_value":"PTK5Q4UADGQA","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PTK5Q4UADGQACEVA","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PTK5Q4UA","created_at":"2026-05-18T12:33:24.271573+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/PTK5Q4UADGQACEVABMJJIX3MRF","json":"https://pith.science/pith/PTK5Q4UADGQACEVABMJJIX3MRF.json","graph_json":"https://pith.science/api/pith-number/PTK5Q4UADGQACEVABMJJIX3MRF/graph.json","events_json":"https://pith.science/api/pith-number/PTK5Q4UADGQACEVABMJJIX3MRF/events.json","paper":"https://pith.science/paper/PTK5Q4UA"},"agent_actions":{"view_html":"https://pith.science/pith/PTK5Q4UADGQACEVABMJJIX3MRF","download_json":"https://pith.science/pith/PTK5Q4UADGQACEVABMJJIX3MRF.json","view_paper":"https://pith.science/paper/PTK5Q4UA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.09820&json=true","fetch_graph":"https://pith.science/api/pith-number/PTK5Q4UADGQACEVABMJJIX3MRF/graph.json","fetch_events":"https://pith.science/api/pith-number/PTK5Q4UADGQACEVABMJJIX3MRF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PTK5Q4UADGQACEVABMJJIX3MRF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PTK5Q4UADGQACEVABMJJIX3MRF/action/storage_attestation","attest_author":"https://pith.science/pith/PTK5Q4UADGQACEVABMJJIX3MRF/action/author_attestation","sign_citation":"https://pith.science/pith/PTK5Q4UADGQACEVABMJJIX3MRF/action/citation_signature","submit_replication":"https://pith.science/pith/PTK5Q4UADGQACEVABMJJIX3MRF/action/replication_record"}},"created_at":"2026-05-17T23:51:38.947593+00:00","updated_at":"2026-05-17T23:51:38.947593+00:00"}