{"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"}