{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:MDWWBNPRNWPTKJ334I3IMEZ6Z6","short_pith_number":"pith:MDWWBNPR","schema_version":"1.0","canonical_sha256":"60ed60b5f16d9f35277be23686133ecfaa6efb1758c75df757c0cd8014e76f6e","source":{"kind":"arxiv","id":"1807.11546","version":1},"attestation_state":"computed","paper":{"title":"Textual Explanations for Self-Driving Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Rohrbach, Jinkyu Kim, John Canny, Trevor Darrell, Zeynep Akata","submitted_at":"2018-07-30T19:38:24Z","abstract_excerpt":"Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations which allow end-users to understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. We propose a new approach to introspective explanations which consists of two parts. First, we use a visual (spatial) attention model to train a convolutional network end-to-end from im"},"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":"1807.11546","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-30T19:38:24Z","cross_cats_sorted":[],"title_canon_sha256":"f2e5d21dd7e69e5fd32699ebe2f8948cf76d894f3ce9a528d312f486e7d2453c","abstract_canon_sha256":"a50fd6faf29e1eeaa793ea2ed3987d206a86f31bc41fc036bae09a2fe3ea9482"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:21.923421Z","signature_b64":"XsjcIMYLnb73k7Vw+iPETTRpH4v2Yi3VXmx6AcyfdvSEA8Md8PkxJTZtAdvL+C7EwhJFWSyilxkjFhauNqD/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60ed60b5f16d9f35277be23686133ecfaa6efb1758c75df757c0cd8014e76f6e","last_reissued_at":"2026-05-18T00:09:21.922996Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:21.922996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Textual Explanations for Self-Driving Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Rohrbach, Jinkyu Kim, John Canny, Trevor Darrell, Zeynep Akata","submitted_at":"2018-07-30T19:38:24Z","abstract_excerpt":"Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations which allow end-users to understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. We propose a new approach to introspective explanations which consists of two parts. First, we use a visual (spatial) attention model to train a convolutional network end-to-end from im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.11546","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":"1807.11546","created_at":"2026-05-18T00:09:21.923058+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.11546v1","created_at":"2026-05-18T00:09:21.923058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.11546","created_at":"2026-05-18T00:09:21.923058+00:00"},{"alias_kind":"pith_short_12","alias_value":"MDWWBNPRNWPT","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"MDWWBNPRNWPTKJ33","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"MDWWBNPR","created_at":"2026-05-18T12:32:37.024351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.05442","citing_title":"Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving","ref_index":41,"is_internal_anchor":true},{"citing_arxiv_id":"2605.00907","citing_title":"TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6","json":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6.json","graph_json":"https://pith.science/api/pith-number/MDWWBNPRNWPTKJ334I3IMEZ6Z6/graph.json","events_json":"https://pith.science/api/pith-number/MDWWBNPRNWPTKJ334I3IMEZ6Z6/events.json","paper":"https://pith.science/paper/MDWWBNPR"},"agent_actions":{"view_html":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6","download_json":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6.json","view_paper":"https://pith.science/paper/MDWWBNPR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.11546&json=true","fetch_graph":"https://pith.science/api/pith-number/MDWWBNPRNWPTKJ334I3IMEZ6Z6/graph.json","fetch_events":"https://pith.science/api/pith-number/MDWWBNPRNWPTKJ334I3IMEZ6Z6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6/action/storage_attestation","attest_author":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6/action/author_attestation","sign_citation":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6/action/citation_signature","submit_replication":"https://pith.science/pith/MDWWBNPRNWPTKJ334I3IMEZ6Z6/action/replication_record"}},"created_at":"2026-05-18T00:09:21.923058+00:00","updated_at":"2026-05-18T00:09:21.923058+00:00"}