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ARXIV:2604.07944 · AUTONOMOUS VEHICLE PLANNING · SUBMITTED 10 APR · 17:37 UTC · FRESHNESS STALE
ARXIV:2604.07944AUTONOMOUS VEHICLE PLANNINGSUBMITTED 10 APR · 17:37 UTCFRESHNESS STALEAmirhossein Afsharrad · Amirhesam Abedsoltan · Ahmadreza Moradipari · Sanjay Lall · arXiv
A method for distilling knowledge from large language models for efficient motion planning in autonomous vehicles.
Opportunity summary
Pain A method for distilling knowledge from large language models for efficient motion planning in autonomous vehicles.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A method for distilling knowledge from large language models for efficient motion planning in autonomous vehicles. However, deploying capable LLMs in resource-constrained onboard systems remains a fundamental challenge.
Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction as a language generation problem. However, deploying capable LLMs in resource-constrained onboard systems remains a fundamental…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the nuScenes benchmark show that GKD substantially outperforms the RL baseline and closely approaches teacher-level performance despite a 5$\times$ reduction in model…
Autonomous Vehicle Planning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A method for distilling knowledge from large language models for efficient motion planning in autonomous vehicles.
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10.48550/arXiv.2604.07944A method for distilling knowledge from large language models for efficient motion planning in autonomous vehicles.
Abstract
Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction as a language generation problem. However, deploying capable LLMs in resource-constrained onboard systems remains a fundamental challenge. In this paper, we study how to effectively transfer motion planning knowledge from a large teacher LLM to a smaller, more deployable student model. We build on the GPT-Driver framework, which represents driving scenes as language prompts and generates waypoint trajectories with chain-of-thought reasoning, and investigate two student training paradigms: (i) on-policy generalized knowledge distillation (GKD), which trains the student on its own self-generated outputs using dense token-level feedback from the teacher, and (ii) a dense-feedback reinforcement learning (RL) baseline that uses the teacher's log-probabilities as per-token reward signals in a policy gradient framework. Experiments on the nuScenes benchmark show that GKD substantially outperforms the RL baseline and closely approaches teacher-level performance despite a 5$\times$ reduction in model size. These results highlight the practical value of on-policy distillation as a principled and effective approach to deploying LLM-based planners in autonomous driving systems.
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unverified0 refs; 3 sources; 50% coverage.
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PROBLEM
A method for distilling knowledge from large language models for efficient motion planning in autonomous vehicles. However, deploying capable LLMs in resource-constrained onboard systems remains a fundamental challenge.
METHOD
Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction as a language generation problem. However, deploying capable LLMs in resource-constrained onboard systems remains a fundamental...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the nuScenes benchmark show that GKD substantially outperforms the RL baseline and closely approaches teacher-level performance despite a 5$\times$ reduction in model size. Code availabilit...
WHY NOW
Autonomous Vehicle Planning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A method for distilling knowledge from large language models for efficient motion planning in autonomous vehicles. However, deploying capable LLMs in resource-constrained onboard systems remains a fundamental challenge.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction as a language generation problem. However, deploying capable LLMs in resource-constrained onboard systems remains a fundamental challenge.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the nuScenes benchmark show that GKD substantially outperforms the RL baseline and closely approaches teacher-level performance despite a 5$\times$ reduction in model size. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Autonomous Vehicle Planning moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A method for distilling knowledge from large language models for efficient motion planning in autonomous vehicles.
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Autonomous Vehicle Planning
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