{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SKLXIQYDM3RS5PX5PLYPXOT25K","short_pith_number":"pith:SKLXIQYD","schema_version":"1.0","canonical_sha256":"929774430366e32ebefd7af0fbba7aea80510d5165b7ed4779b707bdd16e1403","source":{"kind":"arxiv","id":"2304.10891","version":3},"attestation_state":"computed","paper":{"title":"Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Compression strategies for Transformer autonomous driving models must be integrated into system design rather than applied afterward.","cross_cats":["cs.AI","cs.CV","cs.RO","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Juan Zhong, Xi Chen, Yuhang Shi, Zukang Xu","submitted_at":"2023-04-21T11:15:31Z","abstract_excerpt":"Transformer-based models are becoming a central paradigm in autonomous driving because they can capture long-range spatial dependencies, multi-agent interactions, and multimodal context across perception, prediction, and planning. At the same time, their deployment in real vehicles remains difficult because high-capacity attention-based architectures impose substantial latency, memory, and energy overhead. This survey reviews representative Transformer-based autonomous driving models and organizes them by task role, sensing configuration, and architectural design. More importantly, it examines"},"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":"2304.10891","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-21T11:15:31Z","cross_cats_sorted":["cs.AI","cs.CV","cs.RO","cs.SY","eess.SY"],"title_canon_sha256":"591fdad950b3b87ddc5fc0bf498526844d69fa36750244575c77d21e2d2538a3","abstract_canon_sha256":"7544577fa359ac9469a2591331b2efcbe5fe5b29bba411fef3d0be5024bb849e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:08:24.250846Z","signature_b64":"1l8BBy9q3IyIO3sWrNUDht3Dhen3PyPDJSzqBzTtf8W5UZbTYYAjaT5ouSPi2SC2BmyH177nw51TcY2e6R2oAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"929774430366e32ebefd7af0fbba7aea80510d5165b7ed4779b707bdd16e1403","last_reissued_at":"2026-06-04T01:08:24.249466Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:08:24.249466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Compression strategies for Transformer autonomous driving models must be integrated into system design rather than applied afterward.","cross_cats":["cs.AI","cs.CV","cs.RO","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Juan Zhong, Xi Chen, Yuhang Shi, Zukang Xu","submitted_at":"2023-04-21T11:15:31Z","abstract_excerpt":"Transformer-based models are becoming a central paradigm in autonomous driving because they can capture long-range spatial dependencies, multi-agent interactions, and multimodal context across perception, prediction, and planning. At the same time, their deployment in real vehicles remains difficult because high-capacity attention-based architectures impose substantial latency, memory, and energy overhead. This survey reviews representative Transformer-based autonomous driving models and organizes them by task role, sensing configuration, and architectural design. More importantly, it examines"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Rather than treating compression as an isolated post-processing step, we highlight it as a system-level design consideration that directly affects deployability, robustness, and safety.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The survey assumes that the representative models and compression strategies selected from the literature are sufficiently complete and unbiased to support general statements about task-dependent applicability and design trade-offs.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A survey that organizes Transformer-based autonomous driving models by task and architecture while analyzing compression techniques as a system-level deployment concern.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Compression strategies for Transformer autonomous driving models must be integrated into system design rather than applied afterward.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8a971c8a2719d1fde2c77dfe4e9ddf9edcc549001bc91444e63946724caa8906"},"source":{"id":"2304.10891","kind":"arxiv","version":3},"verdict":{"id":"68f58cef-c8ef-4641-a002-f5a3c9ad5423","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-24T09:32:27.712912Z","strongest_claim":"Rather than treating compression as an isolated post-processing step, we highlight it as a system-level design consideration that directly affects deployability, robustness, and safety.","one_line_summary":"A survey that organizes Transformer-based autonomous driving models by task and architecture while analyzing compression techniques as a system-level deployment concern.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The survey assumes that the representative models and compression strategies selected from the literature are sufficiently complete and unbiased to support general statements about task-dependent applicability and design trade-offs.","pith_extraction_headline":"Compression strategies for Transformer autonomous driving models must be integrated into system design rather than applied afterward."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.10891/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":"2304.10891","created_at":"2026-06-04T01:08:24.250145+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.10891v3","created_at":"2026-06-04T01:08:24.250145+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.10891","created_at":"2026-06-04T01:08:24.250145+00:00"},{"alias_kind":"pith_short_12","alias_value":"SKLXIQYDM3RS","created_at":"2026-06-04T01:08:24.250145+00:00"},{"alias_kind":"pith_short_16","alias_value":"SKLXIQYDM3RS5PX5","created_at":"2026-06-04T01:08:24.250145+00:00"},{"alias_kind":"pith_short_8","alias_value":"SKLXIQYD","created_at":"2026-06-04T01:08:24.250145+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/SKLXIQYDM3RS5PX5PLYPXOT25K","json":"https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K.json","graph_json":"https://pith.science/api/pith-number/SKLXIQYDM3RS5PX5PLYPXOT25K/graph.json","events_json":"https://pith.science/api/pith-number/SKLXIQYDM3RS5PX5PLYPXOT25K/events.json","paper":"https://pith.science/paper/SKLXIQYD"},"agent_actions":{"view_html":"https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K","download_json":"https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K.json","view_paper":"https://pith.science/paper/SKLXIQYD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.10891&json=true","fetch_graph":"https://pith.science/api/pith-number/SKLXIQYDM3RS5PX5PLYPXOT25K/graph.json","fetch_events":"https://pith.science/api/pith-number/SKLXIQYDM3RS5PX5PLYPXOT25K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K/action/storage_attestation","attest_author":"https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K/action/author_attestation","sign_citation":"https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K/action/citation_signature","submit_replication":"https://pith.science/pith/SKLXIQYDM3RS5PX5PLYPXOT25K/action/replication_record"}},"created_at":"2026-06-04T01:08:24.250145+00:00","updated_at":"2026-06-04T01:08:24.250145+00:00"}