Diffusion-FS: Multimodal Free-Space Prediction via Diffusion for Autonomous Driving

• URL: http://arxiv.org/abs/2507.18763v1
• Date: Thu, 24 Jul 2025 19:30:55 GMT
• Title: Diffusion-FS: Multimodal Free-Space Prediction via Diffusion for Autonomous Driving
• Authors: Keshav Gupta, Tejas S. Stanley, Pranjal Paul, Arun K. Singh, K. Madhava Krishna,
• Abstract summary: Drivable free-space prediction is a fundamental and crucial problem in autonomous driving.<n>Recent works have addressed the problem by representing the entire non-obstacle road regions as the free-space.<n>Our aim is to estimate the driving corridors that are a navigable subset of the entire road region.
• Score: 7.667821982085968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drivable Free-space prediction is a fundamental and crucial problem in autonomous driving. Recent works have addressed the problem by representing the entire non-obstacle road regions as the free-space. In contrast our aim is to estimate the driving corridors that are a navigable subset of the entire road region. Unfortunately, existing corridor estimation methods directly assume a BEV-centric representation, which is hard to obtain. In contrast, we frame drivable free-space corridor prediction as a pure image perception task, using only monocular camera input. However such a formulation poses several challenges as one doesn't have the corresponding data for such free-space corridor segments in the image. Consequently, we develop a novel self-supervised approach for free-space sample generation by leveraging future ego trajectories and front-view camera images, making the process of visual corridor estimation dependent on the ego trajectory. We then employ a diffusion process to model the distribution of such segments in the image. However, the existing binary mask-based representation for a segment poses many limitations. Therefore, we introduce ContourDiff, a specialized diffusion-based architecture that denoises over contour points rather than relying on binary mask representations, enabling structured and interpretable free-space predictions. We evaluate our approach qualitatively and quantitatively on both nuScenes and CARLA, demonstrating its effectiveness in accurately predicting safe multimodal navigable corridors in the image.

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