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We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles.
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End-to-end interpretable neural motion planner
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Sequential convex programming methods for real-time optimal trajectory planning in autonomous vehicle racing
Patrick Scheffe, Theodor Mario Henneken, Maximilian Kloock, and Bassam Alrifaee · 2022
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
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Ting Chen, Saurabh Saxena, Lala Li, David J Fleet, and Geoffrey Hinton · 2021
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Safe local motion planning with self-supervised freespace forecasting
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Do as i can, not as i say: Grounding language in robotic affordances
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St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning
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Inner monologue: Embodied reasoning through planning with language models
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Differentiable raycasting for self-supervised occupancy forecasting
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Autonomous driving on curvy roads without reliance on frenet frame: A cartesian-based trajectory planning method
Bai Li, Yakun Ouyang, Li Li, and Youmin Zhang · 2022
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
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Parting with misconceptions about learning-based vehicle motion planning
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Planning-oriented autonomous driving
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