The paper proposes combining multidimensional scaling with road-network graph embeddings as feature spaces for generalizable autonomous driving motion planning, but provides only a literature review and toy visualizations, no validated results.
Deep Multi-Sensor Lane Detection
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abstract
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. To address this issue, we propose a novel deep neural network that takes advantage of both LiDAR and camera sensors and produces very accurate estimates directly in 3D space. We demonstrate the performance of our approach on both highways and in cities, and show very accurate estimates in complex scenarios such as heavy traffic (which produces occlusion), fork, merges and intersections.
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cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
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Learning Isometric Embeddings of Road Networks using Multidimensional Scaling
The paper proposes combining multidimensional scaling with road-network graph embeddings as feature spaces for generalizable autonomous driving motion planning, but provides only a literature review and toy visualizations, no validated results.