mlsanityData to 7 Oct 2026

YOLOP: You Only Look Once for Panoptic Driving Perception

arXiv 2108.11250 ↗First posted 25 Aug 2021cs.CVcited by 82

On the map: World models → World models for autonomous driving

Abstract (arXiv)

A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panoptic driving perception network (YOLOP) to perform traffic object detection, drivable area segmentation and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. Our model performs extremely well on the challenging BDD100K dataset, achieving state-of-the-art on all three tasks in terms of accuracy and speed. Besides, we verify the effectiveness of our multi-task learning model for joint training via ablative studies. To our best knowledge, this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS) and maintain excellent accuracy. To facilitate further research, the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.

Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). Placed in World models for autonomous driving, one of 11 directions in World models. Builds on 29 of its 39 references that are in the map. First posted on arXiv 25 Aug 2021.

Builds on

29 of 39 references.

References · 29

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2017Mask R-CNNcited by 7,259
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  6. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  7. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  8. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  9. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  10. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  11. 2015Fast R-CNNcited by 2,930
  12. 2018YOLOv3: An Incremental Improvementcited by 2,652
17 more
  1. 2016Pyramid Scene Parsing Networkcited by 2,472
  2. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  3. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  4. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  5. 2018Path Aggregation Network for Instance Segmentationcited by 796
  6. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 719
  7. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
  8. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  9. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  10. 2016ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentationcited by 291
  11. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  12. 2017Spatial As Deep: Spatial CNN for Traffic Scene Understandingcited by 187
  13. 2019Learning Lightweight Lane Detection CNNs by Self Attention Distillationcited by 162
  14. 2018Towards End-to-End Lane Detection: an Instance Segmentation Approachcited by 129
  15. 2020Ultra Fast Structure-aware Deep Lane Detectioncited by 121
  16. 2016MultiNet: Real-time Joint Semantic Reasoning for Autonomous Drivingcited by 90
  17. 2021Location-Sensitive Visual Recognition with Cross-IOU Losscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 13; 2023: 27; 2024: 15; 2025: 19; 2026: 8.

82 papers.

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  1. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 247
  2. 2024Understanding World or Predicting Future? A Comprehensive Survey of World Modelscited by 161
  3. 2024Visual Adversarial Attack on Vision-Language Models for Autonomous Drivingcited by 35
  4. 2023WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfacescited by 34
  5. 2022HybridNets: End-to-End Perception Networkcited by 30
  6. 2022Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Drivingcited by 30
  7. 2022YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perceptioncited by 25
  8. 2024Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectivescited by 22
  9. 2023Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radarcited by 16
  10. 2022A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillancecited by 14
  11. 2023You Only Look at Once for Real-time and Generic Multi-Taskcited by 14
  12. 2023TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Carscited by 13
38 more
  1. 2024TASeg: Temporal Aggregation Network for LiDAR Semantic Segmentationcited by 13
  2. 2023ASY-VRNet: Waterway Panoptic Driving Perception Model based on Asymmetric Fair Fusion of Vision and 4D mmWave Radarcited by 11
  3. 2022A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perceptioncited by 10
  4. 2025MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perceptioncited by 10
  5. 2023Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Drivingcited by 8
  6. 2023Video Task Decathlon: Unifying Image and Video Tasks in Autonomous Drivingcited by 8
  7. 2022M2-3DLaneNet: Exploring Multi-Modal 3D Lane Detectioncited by 7
  8. 2022Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimationcited by 7
  9. 2023Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Imagescited by 6
  10. 2024YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systemscited by 6
  11. 2024Lane2Seq: Towards Unified Lane Detection via Sequence Generationcited by 5
  12. 2024TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Drivingcited by 5
  13. 2023Dense Hybrid Proposal Modulation for Lane Detectioncited by 4
  14. 2023Image2PCI -- A Multitask Learning Framework for Estimating Pavement Condition Indices Directly from Imagescited by 4
  15. 2023Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalitiescited by 4
  16. 2024Panoptic Perception for Autonomous Driving: A Surveycited by 4
  17. 2024Monocular Lane Detection Based on Deep Learning: A Surveycited by 4
  18. 2025Butter: Frequency Consistency and Hierarchical Fusion for Autonomous Driving Object Detectioncited by 4
  19. 2022Improving Road Segmentation in Challenging Domains Using Similar Place Priorscited by 3
  20. 2025Is Your Autonomous Vehicle Safe? Understanding the Threat of Electromagnetic Signal Injection Attacks on Traffic Scene Perceptioncited by 3
  21. 2025Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systemscited by 3
  22. 2025TEM^3-Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Drivingcited by 3
  23. 2022Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehiclescited by 2
  24. 2023OASIS: Automated Assessment of Urban Pedestrian Paths at Scalecited by 2
  25. 2023Traffic Scene Parsing through the TSP6K Datasetcited by 2
  26. 2023Gated Driver Attention Predictorcited by 2
  27. 2023Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Modelcited by 2
  28. 2024Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Drivingcited by 2
  29. 2025RMT-PPAD: Real-time Multi-task Learning for Panoptic Perception in Autonomous Drivingcited by 2
  30. 2025TriLiteNet: Lightweight Model for Multi-Task Visual Perceptioncited by 2
  31. 2026UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perceptioncited by 2
  32. 2026TwinMixing: A Shuffle-Aware Feature Interaction Model for Multi-Task Segmentationcited by 2
  33. 2022Ego Vehicle Speed Estimation using 3D Convolution with Masked Attentioncited by 1
  34. 2023An intelligent modular real-time vision-based system for environment perceptioncited by 1
  35. 2023DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditionscited by 1
  36. 2023LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learningcited by 1
  37. 2023LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulatorcited by 1
  38. 2024DroneVis: Versatile Computer Vision Library for Dronescited by 1

50 most cited of 82 All 82 (JSON)

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  1. 2024A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions
  2. 2023LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving
  3. 2022DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
  4. 2022Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras
  5. 2025InstanceBEV: Unifying Instance and BEV Representation for 3D Panoptic Segmentation
  6. 2023DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles
  7. 2025FASTopoWM: Fast-Slow Lane Segment Topology Reasoning with Latent World Models
  8. 2025Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey