{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3XYCC5ZVITMMQPASAIKTELZS3I","short_pith_number":"pith:3XYCC5ZV","schema_version":"1.0","canonical_sha256":"ddf021773544d8c83c120215322f32da2b0414093a0e59b8647eaeeb5a5af383","source":{"kind":"arxiv","id":"2405.01394","version":1},"attestation_state":"computed","paper":{"title":"Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Behzad Khamidehi, Chunlin Li, Dhruv Sharma, Eduardo R. Corral-Soto, Fazel Arasteh, Hamidreza Mirkhani, Kasra Rezaee, Mohammed Elmahgiubi, Muhammad Ahsan Kaleem, Tongtong Cao, Weize Zhang","submitted_at":"2024-03-21T23:44:19Z","abstract_excerpt":"In this paper we present the architecture of the Kyber-E2E submission to the map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first place. We employed a modular architecture for our solution consists of five main components: sensing, localization, perception, tracking/prediction, and planning/control. Our solution leverages state-of-the-art language-assisted perception models to help our planner perform more reliably in highly challenging traffic scenarios. We use open-source driving datasets in conjunction with Inverse Reinforcement Learning (IRL) to e"},"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":"2405.01394","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-03-21T23:44:19Z","cross_cats_sorted":[],"title_canon_sha256":"9dab867320bc0b94f080a89c8c7f846c5b32bde61693ee55df159bab8b60f7c1","abstract_canon_sha256":"ae72b3a80f59adc4fd5dde7c0021fc6c00eab6e5cf987ba738eced14fa5363ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:44.626099Z","signature_b64":"fn3M8oQHJPh6fKsAri/fN/4evNZHZ1P/w066u2oEhCqTQBE0jhphoUwScwVOFvNqoE2DdnhLfxAKOrqX5KPICw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ddf021773544d8c83c120215322f32da2b0414093a0e59b8647eaeeb5a5af383","last_reissued_at":"2026-07-05T08:14:44.625550Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:44.625550Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Behzad Khamidehi, Chunlin Li, Dhruv Sharma, Eduardo R. Corral-Soto, Fazel Arasteh, Hamidreza Mirkhani, Kasra Rezaee, Mohammed Elmahgiubi, Muhammad Ahsan Kaleem, Tongtong Cao, Weize Zhang","submitted_at":"2024-03-21T23:44:19Z","abstract_excerpt":"In this paper we present the architecture of the Kyber-E2E submission to the map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first place. We employed a modular architecture for our solution consists of five main components: sensing, localization, perception, tracking/prediction, and planning/control. Our solution leverages state-of-the-art language-assisted perception models to help our planner perform more reliably in highly challenging traffic scenarios. We use open-source driving datasets in conjunction with Inverse Reinforcement Learning (IRL) to e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.01394","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.01394/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":"2405.01394","created_at":"2026-07-05T08:14:44.625620+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.01394v1","created_at":"2026-07-05T08:14:44.625620+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.01394","created_at":"2026-07-05T08:14:44.625620+00:00"},{"alias_kind":"pith_short_12","alias_value":"3XYCC5ZVITMM","created_at":"2026-07-05T08:14:44.625620+00:00"},{"alias_kind":"pith_short_16","alias_value":"3XYCC5ZVITMMQPAS","created_at":"2026-07-05T08:14:44.625620+00:00"},{"alias_kind":"pith_short_8","alias_value":"3XYCC5ZV","created_at":"2026-07-05T08:14:44.625620+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/3XYCC5ZVITMMQPASAIKTELZS3I","json":"https://pith.science/pith/3XYCC5ZVITMMQPASAIKTELZS3I.json","graph_json":"https://pith.science/api/pith-number/3XYCC5ZVITMMQPASAIKTELZS3I/graph.json","events_json":"https://pith.science/api/pith-number/3XYCC5ZVITMMQPASAIKTELZS3I/events.json","paper":"https://pith.science/paper/3XYCC5ZV"},"agent_actions":{"view_html":"https://pith.science/pith/3XYCC5ZVITMMQPASAIKTELZS3I","download_json":"https://pith.science/pith/3XYCC5ZVITMMQPASAIKTELZS3I.json","view_paper":"https://pith.science/paper/3XYCC5ZV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.01394&json=true","fetch_graph":"https://pith.science/api/pith-number/3XYCC5ZVITMMQPASAIKTELZS3I/graph.json","fetch_events":"https://pith.science/api/pith-number/3XYCC5ZVITMMQPASAIKTELZS3I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3XYCC5ZVITMMQPASAIKTELZS3I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3XYCC5ZVITMMQPASAIKTELZS3I/action/storage_attestation","attest_author":"https://pith.science/pith/3XYCC5ZVITMMQPASAIKTELZS3I/action/author_attestation","sign_citation":"https://pith.science/pith/3XYCC5ZVITMMQPASAIKTELZS3I/action/citation_signature","submit_replication":"https://pith.science/pith/3XYCC5ZVITMMQPASAIKTELZS3I/action/replication_record"}},"created_at":"2026-07-05T08:14:44.625620+00:00","updated_at":"2026-07-05T08:14:44.625620+00:00"}