{"ID":10174,"CreatedAt":"2026-02-27T13:00:40Z","UpdatedAt":"2026-02-27T13:00:40Z","DeletedAt":null,"paper_url":"https://paperswithcode.com/paper/towards-end-to-end-lane-detection-an-instance","arxiv_id":"1802.05591","title":"Towards End-to-End Lane Detection: an Instance Segmentation Approach","abstract":"Modern cars are incorporating an increasing number of driver assist features,\namong which automatic lane keeping. The latter allows the car to properly\nposition itself within the road lanes, which is also crucial for any subsequent\nlane departure or trajectory planning decision in fully autonomous cars.\nTraditional lane detection methods rely on a combination of highly-specialized,\nhand-crafted features and heuristics, usually followed by post-processing\ntechniques, that are computationally expensive and prone to scalability due to\nroad scene variations. More recent approaches leverage deep learning models,\ntrained for pixel-wise lane segmentation, even when no markings are present in\nthe image due to their big receptive field. Despite their advantages, these\nmethods are limited to detecting a pre-defined, fixed number of lanes, e.g.\nego-lanes, and can not cope with lane changes. In this paper, we go beyond the\naforementioned limitations and propose to cast the lane detection problem as an\ninstance segmentation problem - in which each lane forms its own instance -\nthat can be trained end-to-end. To parametrize the segmented lane instances\nbefore fitting the lane, we further propose to apply a learned perspective\ntransformation, conditioned on the image, in contrast to a fixed \"bird's-eye\nview\" transformation. By doing so, we ensure a lane fitting which is robust\nagainst road plane changes, unlike existing approaches that rely on a fixed,\npre-defined transformation. In summary, we propose a fast lane detection\nalgorithm, running at 50 fps, which can handle a variable number of lanes and\ncope with lane changes. We verify our method on the tuSimple dataset and\nachieve competitive results.","short_abstract":"By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation.","url_abs":"http://arxiv.org/abs/1802.05591v1","url_pdf":"http://arxiv.org/pdf/1802.05591v1.pdf","authors":"[\"Davy Neven\", \"Bert de Brabandere\", \"Stamatios Georgoulis\", \"Marc Proesmans\", \"Luc van Gool\"]","published":"2018-02-15T00:00:00Z","tasks":"[\"Instance Segmentation\", \"Lane Detection\", \"Semantic Segmentation\"]","methods":"[]","has_code":false,"code_links":[{"ID":13572,"CreatedAt":"2026-02-27T13:01:31Z","UpdatedAt":"2026-02-27T13:01:31Z","DeletedAt":null,"paper_id":10174,"paper_url":"https://paperswithcode.com/paper/towards-end-to-end-lane-detection-an-instance","paper_title":"Towards End-to-End Lane Detection: an Instance Segmentation 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