{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LFE7L7ZJTMROABCN7YKAR7N2Y3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9da60769235fbf73f779fd8b5990dc6990a1f0f8dff42d4d5b09e0706247b8da","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-07-03T03:57:05Z","title_canon_sha256":"4d79a0dcd50ff0953c03e31ed8c546fb77e7c2d7414f4f6fc7e6cf74076e123e"},"schema_version":"1.0","source":{"id":"2407.02797","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.02797","created_at":"2026-07-05T08:39:41Z"},{"alias_kind":"arxiv_version","alias_value":"2407.02797v1","created_at":"2026-07-05T08:39:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02797","created_at":"2026-07-05T08:39:41Z"},{"alias_kind":"pith_short_12","alias_value":"LFE7L7ZJTMRO","created_at":"2026-07-05T08:39:41Z"},{"alias_kind":"pith_short_16","alias_value":"LFE7L7ZJTMROABCN","created_at":"2026-07-05T08:39:41Z"},{"alias_kind":"pith_short_8","alias_value":"LFE7L7ZJ","created_at":"2026-07-05T08:39:41Z"}],"graph_snapshots":[{"event_id":"sha256:bc96421879eb5d342b9444bcfdc95a8644e9af34c22b79fef0baaf1b08f3da27","target":"graph","created_at":"2026-07-05T08:39:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2407.02797/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As autonomous driving systems being deployed to millions of vehicles, there is a pressing need of improving the system's scalability, safety and reducing the engineering cost. A realistic, scalable, and practical simulator of the driving world is highly desired. In this paper, we present an efficient solution based on generative models which learns the dynamics of the driving scenes. With this model, we can not only simulate the diverse futures of a given driving scenario but also generate a variety of driving scenarios conditioned on various prompts. Our innovative design allows the model to ","authors_text":"Haichao Zhang, Jingyu Qian, Kun Li, Qiang Liu, Siqi Chai, Wei Xu, Wenxin Shao, Yihan Hu, Zhening Yang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-07-03T03:57:05Z","title":"Solving Motion Planning Tasks with a Scalable Generative Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02797","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2879e508bd4f5eabe2bce663ea1aeef8e55dd16cc2ae3a89e9ebfbe25b57395a","target":"record","created_at":"2026-07-05T08:39:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9da60769235fbf73f779fd8b5990dc6990a1f0f8dff42d4d5b09e0706247b8da","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-07-03T03:57:05Z","title_canon_sha256":"4d79a0dcd50ff0953c03e31ed8c546fb77e7c2d7414f4f6fc7e6cf74076e123e"},"schema_version":"1.0","source":{"id":"2407.02797","kind":"arxiv","version":1}},"canonical_sha256":"5949f5ff299b22e0044dfe1408fdbac6e878c55418eaa69be7fb75054fe15b6f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5949f5ff299b22e0044dfe1408fdbac6e878c55418eaa69be7fb75054fe15b6f","first_computed_at":"2026-07-05T08:39:41.886412Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:39:41.886412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GezY47AoESl55oXShfyrYHdICSEvIFbYV2qYAJiT8xA+oh5+07a+S/5118eLVvWmliTniw5PtyjgEFcGqJVUCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:39:41.886856Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.02797","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2879e508bd4f5eabe2bce663ea1aeef8e55dd16cc2ae3a89e9ebfbe25b57395a","sha256:bc96421879eb5d342b9444bcfdc95a8644e9af34c22b79fef0baaf1b08f3da27"],"state_sha256":"fab0fdb4ada41c2ebf77ba39ed56193630f0bece6654bbb56b1954f85fdfc735"}