{"paper":{"title":"Accelerating Time-Optimal Trajectory Planning for Connected and Automated Vehicles with Graph Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A graph neural network provides warm starts that let numerical optimizers compute time-optimal trajectories for connected automated vehicles much faster.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Andreas A. Malikopoulos, Viet-Anh Le","submitted_at":"2025-11-25T15:05:49Z","abstract_excerpt":"In this paper, we present a learning-based framework that accelerates time- and energy-optimal trajectory planning for connected and automated vehicles (CAVs) using graph neural networks (GNNs). We formulate the multi-agent coordination problem encountered in traffic scenarios as a cooperative trajectory planning problem that minimizes travel time, subject to motion primitives derived from energy-optimal solutions. The performance of this framework can be further improved through replanning at each time step, enabling the system to incorporate newly observed information. To achieve real-time e"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The trained model produces online predictions that serve as warm-starts for numerical optimization, thereby enabling rapid computation of minimal exit times and the associated feasible trajectories.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the graph neural network trained on offline-generated data will generalize to produce effective warm starts for previously unseen online traffic configurations without degrading the quality or feasibility of the resulting optimized trajectories.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A graph isomorphism network with edge features is trained on offline data to provide warm starts for numerical optimization of time-optimal multi-agent trajectories in connected automated vehicles, reducing computation time while preserving performance through replanning.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A graph neural network provides warm starts that let numerical optimizers compute time-optimal trajectories for connected automated vehicles much faster.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b503ecc374883fd7405208850d26d8555fda549487dfd3ef7f603a2fc50c8a06"},"source":{"id":"2511.20383","kind":"arxiv","version":2},"verdict":{"id":"024f0873-f045-4e1c-94d1-c24cd56b9135","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T04:31:01.711619Z","strongest_claim":"The trained model produces online predictions that serve as warm-starts for numerical optimization, thereby enabling rapid computation of minimal exit times and the associated feasible trajectories.","one_line_summary":"A graph isomorphism network with edge features is trained on offline data to provide warm starts for numerical optimization of time-optimal multi-agent trajectories in connected automated vehicles, reducing computation time while preserving performance through replanning.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the graph neural network trained on offline-generated data will generalize to produce effective warm starts for previously unseen online traffic configurations without degrading the quality or feasibility of the resulting optimized trajectories.","pith_extraction_headline":"A graph neural network provides warm starts that let numerical optimizers compute time-optimal trajectories for connected automated vehicles much faster."},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"4d8cbb882135880c505ab79ebd9778a835cc8031b52125c811332d6691efb5ac"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}