Papers › PolyLaneNet: Lane Estimation via Deep Polynomial Regression

PolyLaneNet: Lane Estimation via Deep Polynomial Regression

23 Apr 2020arXiv 2020 4arXiv:2004.10924archive 2025-07-28

Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos

One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning. For safer self-driving vehicles, one of the problems that has yet to be solved completely is lane detection. Since methods for this task have to work in real-time (+30 FPS), they not only have to be effective (i.e., have high accuracy) but they also have to be efficient (i.e., fast). In this work, we present a novel method for lane detection that uses as input an image from a forward-looking camera mounted in the vehicle and outputs polynomials representing each lane marking in the image, via deep polynomial regression. The proposed method is shown to be competitive with existing state-of-the-art methods in the TuSimple dataset while maintaining its efficiency (115 FPS). Additionally, extensive qualitative results on two additional public datasets are presented, alongside with limitations in the evaluation metrics used by recent works for lane detection. Finally, we provide source code and trained models that allow others to replicate all the results shown in this paper, which is surprisingly rare in state-of-the-art lane detection methods. The full source code and pretrained models are available at https://github.com/lucastabelini/PolyLaneNet.

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area_distance lucastabelini/PolyLaneNet/utils/metric.py official repository unverified MIT (permissive) · 4648a2af225d49fd · report
area_metric lucastabelini/PolyLaneNet/utils/metric.py official repository unverified MIT (permissive) · 7b9f75652d0c8239 · report
convert_tusimple_format lucastabelini/PolyLaneNet/utils/metric.py official repository unverified MIT (permissive) · f4aa5ab4bed99b4b · report
create_video lucastabelini/PolyLaneNet/utils/gen_video.py official repository unverified MIT (permissive) · fe37f79f76eab305 · report
get_horizontal_values_for_four_lanes lucastabelini/PolyLaneNet/lib/datasets/llamas.py official repository unverified MIT (permissive) · 42d07e7a73ed1b03 · report
ir lucastabelini/PolyLaneNet/lib/datasets/llamas.py official repository unverified MIT (permissive) · 51d50b31b8fe13e0 · report
parse_line lucastabelini/PolyLaneNet/utils/plot_log.py official repository unverified MIT (permissive) · a3d164c22a005dc7 · report
parse_log lucastabelini/PolyLaneNet/utils/plot_log.py official repository unverified MIT (permissive) · d1c3c14d98baf762 · report
read_json lucastabelini/PolyLaneNet/lib/datasets/llamas.py official repository unverified MIT (permissive) · 1cf08d93a9b54189 · report
smooth_curve lucastabelini/PolyLaneNet/utils/plot_log.py official repository unverified MIT (permissive) · e93e343e68c08583 · report

Tasks

Autonomous DrivingLane Detectionregression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Lane Detection LLAMAS PolyLaneNet F1 0.8840 #9 of 10 Archive leaderboard report
Lane Detection TuSimple PolyLaneNet Accuracy 93.36% #38 of 43 Archive leaderboard report
Lane Detection TuSimple PolyLaneNet F1 score 90.62 #38 of 43 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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