# Towards Driving-Oriented Metric for Lane Detection Models arXiv: https://arxiv.org/abs/2203.16851 · HTML: https://mlsanity.com/paper/2203.16851/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in VLMs for autonomous driving, one of 9 directions in End-to-end autonomous driving. - Builds on 30 of its 59 references that are in the map. - First posted on arXiv 31 Mar 2022. ## Placed in - End-to-end autonomous driving / [VLMs for autonomous driving](https://mlsanity.com/driving/vlms-for-autonomous-driving/) ## Builds on (most cited first, 30 of 30) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014) - [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018) - [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016) - [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019) - [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017) - [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018) - [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019) - [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://mlsanity.com/paper/2101.06806/) (2021) - [Differentiable MPC for End-to-end Planning and Control](https://mlsanity.com/paper/1810.13400/) (2018) - [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017) - [Physical Adversarial Examples for Object Detectors](https://mlsanity.com/paper/1807.07769/) (2018) - [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://mlsanity.com/paper/1908.00821/) (2019) - [Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection](https://mlsanity.com/paper/2010.12035/) (2020) - [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018) - [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020) - [PolyLaneNet: Lane Estimation via Deep Polynomial Regression](https://mlsanity.com/paper/2004.10924/) (2020) - [CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution](https://mlsanity.com/paper/2105.05003/) (2021) - [RESA: Recurrent Feature-Shift Aggregator for Lane Detection](https://mlsanity.com/paper/2008.13719/) (2020) - [Focus on Local: Detecting Lane Marker from Bottom Up via Key Point](https://mlsanity.com/paper/2105.13680/) (2021) - [Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack](https://mlsanity.com/paper/2009.06701/) (2020) - [Inter-Region Affinity Distillation for Road Marking Segmentation](https://mlsanity.com/paper/2004.05304/) (2020) - [End-to-End Lane Marker Detection via Row-wise Classification](https://mlsanity.com/paper/2005.08630/) (2020) - [FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network](https://mlsanity.com/paper/1905.04354/) (2019) - [A Commute in Data: The comma2k19 Dataset](https://mlsanity.com/paper/1812.05752/) (2018) - [Autonomy 2.0: Why is self-driving always 5 years away?](https://mlsanity.com/paper/2107.08142/) (2021) - [Learning to Cluster for Proposal-Free Instance Segmentation](https://mlsanity.com/paper/1803.06459/) (2018) ## Built on by (most cited first, 7 of 7) - [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022) - [LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving](https://mlsanity.com/paper/2407.07740/) (2024) - [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024) - [RowDetr: End-to-End Crop Row Detection Using Polynomials](https://mlsanity.com/paper/2412.10525/) (2024) - [Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2504.08540/) (2025) - [EPSM: A Novel Metric to Evaluate the Safety of Environmental Perception in Autonomous Driving](https://mlsanity.com/paper/2512.15195/) (2025) - [Real-Time Lane Detection via Efficient Feature Alignment and Covariance Optimization for Low-Power Embedded Systems](https://mlsanity.com/paper/2601.01696/) (2026) ## Abstract (arXiv) After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation based on accuracy and F1 score have become the de facto standard to measure the performance of lane detection methods. While they have played a major role in improving the performance of lane detection methods, the validity of this evaluation method in downstream tasks has not been adequately researched. In this study, we design 2 new driving-oriented metrics for lane detection: End-to-End Lateral Deviation metric (E2E-LD) is directly formulated based on the requirements of autonomous driving, a core downstream task of lane detection; Per-frame Simulated Lateral Deviation metric (PSLD) is a lightweight surrogate metric of E2E-LD. To evaluate the validity of the metrics, we conduct a large-scale empirical study with 4 major types of lane detection approaches on the TuSimple dataset and our newly constructed dataset Comma2k19-LD. Our results show that the conventional metrics have strongly negative correlations (≤-0.55) with E2E-LD, meaning that some recent improvements purely targeting the conventional metrics may not have led to meaningful improvements in autonomous driving, but rather may actually have made it worse by overfitting to the conventional metrics. As autonomous driving is a security/safety-critical system, the underestimation of robustness hinders the sound development of practical lane detection models. We hope that our study will help the community achieve more downstream task-aware evaluations for lane detection.