Drivable Space in Autonomous Driving — The Academia | by Patrick Langechuan Liu – Towards Data Science

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Patrick Langechuan Liu
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Drivable space, or Free Space, plays a safety-critical role in autonomous driving. In a previous blog post, we reviewed the definition and importance of this often-overlooked perception feature. In this article, we will review recent trends in academic research.
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Drivable space detection algorithms can be measured in two dimensions: inputs and outputs. Regarding the input sensor modality, drivable space detection methods can be categorized as vision-based, LiDAR-based, or vision-LiDAR fusion methods. Regarding the output spatial representation, they can be categorized as 2D perspective image space, 3D space, and Bird’s Eye View (BEV) space.
Visual images are intrinsically 2D, and lidar point cloud measurements are intrinsically 3D. As discussed in a previous blog post, BEV space is essentially a simplified or degenerate 3D space, and we will use BEV space and 3D space interchangeably throughout this blog. In essence, we have a 2×2 input-output matrix for the evaluation of all drivable space algorithms, as shown in the image below. The top right quadrant in green is the North Star which has the best representation power while being the most cost-effective. In the following sections, we will discuss various algorithms that fall into three categories: 2D-to-2D, 3D-to-3D, and 2D-to-3D.
It is worth noting that there is currently no universally recognized standard for expressing and evaluating the accuracy of drivable space in autonomous driving. In this post, we will review related tasks, which can take a variety of formulations. We also provide some insights on the future direction of the academic community, with the aim of accelerating research on this crucial task in autonomous driving.
The detection of drivable space in perspective 2D image space is essentially the task of image segmentation…
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Towards Data Science
A physicist-turned AI research engineer. AI for autonomous driving at Xpeng Motors. www.linkedin.com/in/patrick-llgc
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