AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via
  Reinforcement Learning and Reasoning
OpenAI o1 and DeepSeek R1 achieve or even surpass human expert-level
performance in complex domains like mathematics and science, with reinforcement
learning (RL) and reasoning playing a crucial role. In autonomous driving,
recent end-to-end models have greatly improved planning performance but still
struggle with long-tailed problems due to limited common sense and reasoning
abilities. Some studies integrate vision-language models (VLMs) into autonomous
driving, but they typically rely on pre-trained models with simple supervised
fine-tuning (SFT) on driving data, without further exploration of training
strategies or optimizations specifically tailored for planning. In this paper,
we propose AlphaDrive, a RL and reasoning framework for VLMs in autonomous
driving. AlphaDrive introduces four GRPO-based RL rewards tailored for planning
and employs a two-stage planning reasoning training strategy that combines SFT
with RL. As a result, AlphaDrive significantly improves both planning
performance and training efficiency compared to using only SFT or without
reasoning. Moreover, we are also excited to discover that, following RL
training, AlphaDrive exhibits some emergent multimodal planning capabilities,
which is critical for improving driving safety and efficiency. To the best of
our knowledge, AlphaDrive is the first to integrate GRPO-based RL with planning
reasoning into autonomous driving. Code will be released to facilitate future
research.
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Bo Jiang
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2025-03-11T08:22:43.665Z
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Bo Jiang
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Shaoyu Chen
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2025-03-11T11:16:49.557Z
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Shaoyu Chen
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Qian Zhang
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Wenyu Liu
admin_assigned
2025-03-11T11:17:02.692Z
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LWY
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wenyuliu
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Xinggang Wang
admin_assigned
2025-03-11T11:16:56.180Z
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Xinggang Wang
user
xinggangw
AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via
  Reinforcement Learning and Reasoning
OpenAI o1 and DeepSeek R1 achieve or even surpass human expert-level
performance in complex domains like mathematics and science, with reinforcement
learning (RL) and reasoning playing a crucial role. In autonomous driving,
recent end-to-end models have greatly improved planning performance but still
struggle with long-tailed problems due to limited common sense and reasoning
abilities. Some studies integrate vision-language models (VLMs) into autonomous
driving, but they typically rely on pre-trained models with simple supervised
fine-tuning (SFT) on driving data, without further exploration of training
strategies or optimizations specifically tailored for planning. In this paper,
we propose AlphaDrive, a RL and reasoning framework for VLMs in autonomous
driving. AlphaDrive introduces four GRPO-based RL rewards tailored for planning
and employs a two-stage planning reasoning training strategy that combines SFT
with RL. As a result, AlphaDrive significantly improves both planning
performance and training efficiency compared to using only SFT or without
reasoning. Moreover, we are also excited to discover that, following RL
training, AlphaDrive exhibits some emergent multimodal planning capabilities,
which is critical for improving driving safety and efficiency. To the best of
our knowledge, AlphaDrive is the first to integrate GRPO-based RL with planning
reasoning into autonomous driving. Code will be released to facilitate future
research.
AlphaDrive, a reinforcement learning and reasoning framework, enhances planning performance and training efficiency in autonomous driving by integrating GRPO-based RL rewards and a two-stage training strategy.
reinforcement learning (RL) reasoning autonomous driving end-to-end models long-tailed problems vision-language models (VLMs) simple supervised fine-tuning (SFT) GRPO-based RL rewards two-stage planning reasoning training strategy emergent multimodal planning capabilities