Lane Detection

Latest papers

14 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Silence Is Not Assurance: When Production Lane Keeping Assist Does Not Warn, and What It Costs
Oct 22, 2026Tianjia Yang, Yuxin Ding, Ke Li +3Autonomous Driving Safety Evaluation Autonomous Driving
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA · Department of Civil Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Edge Deployable Lightweight Framework for Real-Time Driver Safety System with Integrated Lane Departure and Traffic Sign Recognition
Oct 18, 2026Hassaan Ali Qureshi, Danish Hussain, Ali Hassan +5Traffic Sign Detection Autonomous Driving Perception
Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Department of Robotics and Artificial Intelligence, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan +2
Learning Lane-Structured Conditioning Fields for Online HD Mapping
Oct 18, 2026Andrea Bertogalli, Anthony Dell'Eva, Marcello Ceresini +5HD Map Construction Lane Detection
University of Parma, Italy · VisLab, Italy · University of Bologna, Italy
Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection
Aug 10, 2026Weize Cai, Yongqi Dong, Zhida Shao +2Lane Detection Autonomous Driving
RWTH Aachen University, Aachen, Germany · Delft University of Technology, Delft, The Netherlands · Chang’an University, Xi’an, China
WASABI: Whole-graph Assignment-based Stabilizer for lAne topology By Inter-frame tracking
Jul 22, 2026Tetsuhiro Uchida, Myu Sasaki, Kensho Nakajima +2Autonomous Driving Perception Temporal Consistency
Sony Honda Mobility Inc.
3D Lane Detection with Odometry for High-Speed Vehicle Racing
Jul 15, 2026Omoruyi Atekha, John Subosits, Marcus GreiffAutonomous Driving Datasets Autonomous Driving Perception
Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. · Toyota Research Institute (TRI), 94022 Los Altos, CA, USA.
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective
Jul 7, 2026Tianyuan Zhang, Xianglong Liu, Aishan Liu +6Autonomous Driving Benchmarks VLM Robustness
SKLCCSE, the School of Computer Science and Engineering, Beihang University, Beijing 100191, China · School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, China · Henan University of Science and Technology, Luoyang 471023, China +2
GFSR: Geometric Fidelity and Spatial Refinement for Reliable Lane Detection
May 22, 2026Tiancheng Wang, Zhaolu Ding, Richeng Xu +5Probability Calibration Lane Detection
the School of Big Data and Statistics, Anhui University, Hefei 230039, China · the School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China · Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China +2
Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning
May 9, 2026Han Li, Yulu Gao, Si Liu +3Lane Detection Autonomous Driving Perception
School of Artificial Intelligence, Beihang University, Beijing, China · Zhongguancun Academy, Beijing, China · Hangzhou International Innovation Institute, Beihang University, Hangzhou, China +1
MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling
Apr 13, 2026Mingyang Li, Brian Lee, Rui Zuo +3Lane Detection HD Map Construction
Department of Engineering and Computer Science, Syracuse University · Research and Development, General Motors
Lightweight Spatiotemporal Highway Lane Detection via 3D-ResNet and PINet with ROI-Aware Attention
Apr 2, 2026Sorna Shanmuga Raja, Abdelhafid ZenatiLane Detection Autonomous Driving Perception
Engineering Department, School of Science and Technology (SST), City, St George’s University of London, Northampton Square, London, EC1V 0HB, United Kingdom.
PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving
Mar 24, 2026Yasamin Borhani, Taylor Mordan, Yihan Wang +3Lane Detection Autonomous Driving Perception
Ecole Polytechnique Federale de Lausanne (EPFL)
HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation
Mar 10, 2026Daichao Zhao, Qiupu Chen, Feng He +2Lane Detection Autonomous Driving Datasets
Shanghai Jiao Tong University · School of Artificial Intelligence, Henan University · University of Science and Technology of China +2
Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning
Mar 10, 2026Kanishkha Jaisankar, Pranav M. Pawar, Diana Susan Joseph +4Lane Detection Vehicle Detection
Department of Computer Science, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, UAE. · Sinhgad Institute of Technology, Lonavala · CTIF Global Capsule, Denmark

Oct 22, 2026J/K move · Enter open · S save

Silence Is Not Assurance: When Production Lane Keeping Assist Does Not Warn, and What It Costs

Tianjia Yang, Yuxin Ding, Ke Li, Kaidi Liang+2

Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA · Department of Civil Engineering, Stony Brook University, Stony Brook, NY 11794, USA

Autonomous Driving Safety Evaluation Autonomous Driving Lane Detection

Open paper AskPDF arXiv

Production lane keeping assist (LKA) systems tell the driver whether they detect the lane markings, but whether this displayed status reflects what the system perceives, and what happens when it does not, has not been examined in real-world driving. This study introduces CANDID-LKA, a CAN-based Diagnosis of the Indicated Detection status in LKA. It evaluates a proprietary driver-facing status using three signals: the displayed status and the steering torque of the driver, both recorded from the controller area network (CAN) bus of the vehicle, and the lane perception confidence of a third-party vision system observing the same road. The framework examines how the status responds to driving conditions and estimates how completely it reports degraded lane perception by comparing it with the other two signals in a latent-class model. We apply it to 39,800 seconds of real-world driving from 13 vehicle models of three manufacturers. The results show that the status does not respond consistently to the conditions that make markings harder to see, and that the three makes differ widely in how often they report not detected. The latent-class model shows that the displayed status is incomplete: it reports only 47% of degraded seconds as not detected, compared with 80% for the third-party low perception confidence, and in half of the low-confidence episodes it never reports not detected. In the silent seconds in which the status reads detected while the perception confidence is low and the driver is not in control, the standard deviation of lateral position over the next 3 s is about 60% larger than when the status and the perception confidence agree. A detected status thus does not guarantee reliable lane perception, and silence with the driver not in control is the case that most needs attention.