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.
Abstract (translated)
可行驶自由空间预测是自动驾驶中的一个基础且关键的问题。近期的工作通过将整个无障碍的路面区域表示为自由空间来解决这个问题。相比之下，我们的目标是估计出驾驶通道，这是整个道路区域内可导航的一个子集。不幸的是，现有的通道估算方法直接假设了一个以鸟瞰图（BEV）为中心的表示方式，这种表示方式难以获得。因此，我们将可行驶自由空间通道预测定义为一个纯粹基于图像感知的任务，并且仅使用单目相机输入来进行这项任务。然而，这样的设定带来了几个挑战，因为没有相应的数据来描述这些存在于图像中的自由空间通道片段。 为了应对这一问题，我们开发了一种新颖的自我监督方法，用于生成自由空间样本，这种方法利用了未来的自车轨迹和前视图相机图像，并且将视觉通道估计过程与自车轨迹联系起来。接着，我们使用扩散过程来建模这些在图像中的片段分布情况。 然而，现有的基于二值掩码表示方式的方法存在许多限制。因此，我们引入了一种名为ContourDiff的专门化架构，这是一种基于扩散技术的设计，在降噪时采用轮廓点而非依赖于传统的二进制掩码表示方法，从而能够生成结构清晰且易于解释的自由空间预测结果。 我们在nuScenes和CARLA数据集上对我们的方法进行了定性和定量评估，展示了其在准确预测图像中安全多模态可导航通道方面的能力。
URL
https://arxiv.org/abs/2507.18763
