StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving
Vision Language Models (VLMs) bridge visual perception and linguistic reasoning. In Autonomous Driving (AD), this synergy has enabled Vision Language Action (VLA) models, which translate high-level multimodal understanding into driving behaviors, typically represented as future trajectories. However, existing VLA models mainly generate generic collision-free trajectories. Beyond collision avoidance, adapting to diverse driving styles (e.g., sporty, comfortable) is essential for personalized driving. Moreover, many methods treat trajectory generation as naive token prediction, which can produce kinematically infeasible actions. To address these limitations, we present StyleVLA, a physics-informed VLA framework for generating diverse and physically plausible driving behaviors. We introduce a hybrid loss that combines a kinematic consistency constraint with a continuous regression head to improve trajectory feasibility. To train StyleVLA, built on Qwen3-VL-4B, we construct a large-scale instruction dataset with over 1.2k scenarios, 76k Bird's Eye View (BEV) samples, and 42k First Person View (FPV) samples, with ground-truth trajectories for five driving styles and natural-language instructions. Experiments show that our 4B-parameter StyleVLA significantly outperforms proprietary models (e.g., Gemini-3-Pro) and state-of-the-art VLA models. Using a composite driving score measuring success rate, physical feasibility, and style adherence, StyleVLA achieves 0.55 on BEV and 0.51 on FPV, versus 0.32 and 0.35 for Gemini-3-Pro. These results show that a specialized, physics-informed, lightweight model can surpass closed-source models on domain-specific tasks.
69b1529b1f7902d47f6dfc29
Yuan Gao
claimed_verified
2026-03-13T08:14:34.820Z
672288dd7055eec76d377268
https://cdn-avatars.huggingface.co/v1/production/uploads/672288dd7055eec76d377268/bBQ51VwKZJIkHkNffuEuJ.jpeg
Yuan Gao
Yuan-avs
user
Yuan-avs
69b1529b1f7902d47f6dfc2a
Dengyuan Hua
claimed_verified
2026-03-16T10:44:02.626Z
662a4ca2c9cfc4bb3bba33d3
/avatars/063d2b25afca78e7a28451db9a176e0d.svg
Hua Dengyuan
LeoHuaDY
user
LeoHuaDY
69b1529b1f7902d47f6dfc2b
Mattia Piccinini
69b1529b1f7902d47f6dfc2c
Finn Rasmus Schäfer
69b1529b1f7902d47f6dfc2d
Korbinian Moller
69b1529b1f7902d47f6dfc2e
Lin Li
69b1529b1f7902d47f6dfc2f
Johannes Betz
StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving
Vision Language Models (VLMs) bridge visual perception and linguistic reasoning. In Autonomous Driving (AD), this synergy has enabled Vision Language Action (VLA) models, which translate high-level multimodal understanding into driving behaviors, typically represented as future trajectories. However, existing VLA models mainly generate generic collision-free trajectories. Beyond collision avoidance, adapting to diverse driving styles (e.g., sporty, comfortable) is essential for personalized driving. Moreover, many methods treat trajectory generation as naive token prediction, which can produce kinematically infeasible actions. To address these limitations, we present StyleVLA, a physics-informed VLA framework for generating diverse and physically plausible driving behaviors. We introduce a hybrid loss that combines a kinematic consistency constraint with a continuous regression head to improve trajectory feasibility. To train StyleVLA, built on Qwen3-VL-4B, we construct a large-scale instruction dataset with over 1.2k scenarios, 76k Bird's Eye View (BEV) samples, and 42k First Person View (FPV) samples, with ground-truth trajectories for five driving styles and natural-language instructions. Experiments show that our 4B-parameter StyleVLA significantly outperforms proprietary models (e.g., Gemini-3-Pro) and state-of-the-art VLA models. Using a composite driving score measuring success rate, physical feasibility, and style adherence, StyleVLA achieves 0.55 on BEV and 0.51 on FPV, versus 0.32 and 0.35 for Gemini-3-Pro. These results show that a specialized, physics-informed, lightweight model can surpass closed-source models on domain-specific tasks.
StyleVLA is a physics-informed vision-language-action framework that generates diverse and physically plausible driving trajectories by combining kinematic constraints with continuous regression, outperforming proprietary models on specialized autonomous driving tasks.
Vision Language Models Vision Language Action models trajectory generation kinematic consistency continuous regression head physics-informed modeling instruction dataset Bird's Eye View First Person View driving styles composite driving score