{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:T4SXF6LW3GYLZO6KWLVEQDARHB","short_pith_number":"pith:T4SXF6LW","schema_version":"1.0","canonical_sha256":"9f2572f976d9b0bcbbcab2ea480c113862cd6746a2a408aaa198bfdca71a5853","source":{"kind":"arxiv","id":"2403.19461","version":2},"attestation_state":"computed","paper":{"title":"Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Arun Kumar Singh, Basant Sharma, Simon Idoko","submitted_at":"2024-03-28T14:32:57Z","abstract_excerpt":"Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribution through generative models such as Conditional Variational Autoencoder (CVAE). However, these approaches fail to capture the multi-modality of the driving behaviour due to the Gaussian latent prior of the CVAE. Thus, in this paper, we re-imagine the distribution learning through vector quantized"},"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":"2403.19461","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-03-28T14:32:57Z","cross_cats_sorted":[],"title_canon_sha256":"5f380fff82161a4e6f844755b7358a2aec3941088b249031cf7fe54de558c7eb","abstract_canon_sha256":"fc2ae08c3641910161b16774babd29f755d829a9f7e522268169e040a6caaf7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:58.689731Z","signature_b64":"D2x6aRWxb1DFwA+aEaGBe2Wtg50NuVq9ac2N4bIr8xScnsIf5O6WAnBS2+U7V1/1lFOqQsVaWN56Fz55v0TTBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f2572f976d9b0bcbbcab2ea480c113862cd6746a2a408aaa198bfdca71a5853","last_reissued_at":"2026-07-05T08:11:58.689206Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:58.689206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Arun Kumar Singh, Basant Sharma, Simon Idoko","submitted_at":"2024-03-28T14:32:57Z","abstract_excerpt":"Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribution through generative models such as Conditional Variational Autoencoder (CVAE). However, these approaches fail to capture the multi-modality of the driving behaviour due to the Gaussian latent prior of the CVAE. Thus, in this paper, we re-imagine the distribution learning through vector quantized"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19461","kind":"arxiv","version":2},"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/2403.19461/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":"2403.19461","created_at":"2026-07-05T08:11:58.689267+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.19461v2","created_at":"2026-07-05T08:11:58.689267+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19461","created_at":"2026-07-05T08:11:58.689267+00:00"},{"alias_kind":"pith_short_12","alias_value":"T4SXF6LW3GYL","created_at":"2026-07-05T08:11:58.689267+00:00"},{"alias_kind":"pith_short_16","alias_value":"T4SXF6LW3GYLZO6K","created_at":"2026-07-05T08:11:58.689267+00:00"},{"alias_kind":"pith_short_8","alias_value":"T4SXF6LW","created_at":"2026-07-05T08:11:58.689267+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.19042","citing_title":"Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB","json":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB.json","graph_json":"https://pith.science/api/pith-number/T4SXF6LW3GYLZO6KWLVEQDARHB/graph.json","events_json":"https://pith.science/api/pith-number/T4SXF6LW3GYLZO6KWLVEQDARHB/events.json","paper":"https://pith.science/paper/T4SXF6LW"},"agent_actions":{"view_html":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB","download_json":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB.json","view_paper":"https://pith.science/paper/T4SXF6LW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.19461&json=true","fetch_graph":"https://pith.science/api/pith-number/T4SXF6LW3GYLZO6KWLVEQDARHB/graph.json","fetch_events":"https://pith.science/api/pith-number/T4SXF6LW3GYLZO6KWLVEQDARHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB/action/storage_attestation","attest_author":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB/action/author_attestation","sign_citation":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB/action/citation_signature","submit_replication":"https://pith.science/pith/T4SXF6LW3GYLZO6KWLVEQDARHB/action/replication_record"}},"created_at":"2026-07-05T08:11:58.689267+00:00","updated_at":"2026-07-05T08:11:58.689267+00:00"}