Accelerating Time-Optimal Trajectory Planning for Connected and Automated Vehicles with Graph Neural Networks
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| Format: | Preprint |
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2025
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| _version_ | 1866918498963292160 |
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| author | Le, Viet-Anh Malikopoulos, Andreas A. |
| author_facet | Le, Viet-Anh Malikopoulos, Andreas A. |
| contents | 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 execution, we employ a graph isomorphism network with edge features (GINEConv) to learn the solutions of the time-optimal trajectory planning problem from offline-generated data. 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. This learning-to-warm-start approach substantially reduces computation time while preserving the control performance of the time- and energy-optimal trajectory planning framework. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_20383 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Time-Optimal Trajectory Planning for Connected and Automated Vehicles with Graph Neural Networks Le, Viet-Anh Malikopoulos, Andreas A. Systems and Control 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 execution, we employ a graph isomorphism network with edge features (GINEConv) to learn the solutions of the time-optimal trajectory planning problem from offline-generated data. 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. This learning-to-warm-start approach substantially reduces computation time while preserving the control performance of the time- and energy-optimal trajectory planning framework. |
| title | Accelerating Time-Optimal Trajectory Planning for Connected and Automated Vehicles with Graph Neural Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.20383 |