mlsanityData to 7 Oct 2026

Object Detection in Autonomous Vehicles: Status and Open Challenges

arXiv 2201.07706 ↗First posted 19 Jan 2022cs.CV cs.AI cs.LGcited by 14

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Abstract (arXiv)

Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans. Object detection is also one of the critical components to support autonomous driving. Autonomous vehicles rely on the perception of their surroundings to ensure safe and robust driving performance. This perception system uses object detection algorithms to accurately determine objects such as pedestrians, vehicles, traffic signs, and barriers in the vehicle's vicinity. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 29 of its 60 references that are in the map. First posted on arXiv 19 Jan 2022.

Builds on

29 of 60 references.

References · 29

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  4. 2015Fast R-CNNcited by 2,930
  5. 2018YOLOv3: An Incremental Improvementcited by 2,652
  6. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  7. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  8. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
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17 more
  1. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
  2. 2021DetCo: Unsupervised Contrastive Learning for Object Detectioncited by 205
  3. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  4. 2019A Survey of Deep Learning-based Object Detectioncited by 106
  5. 2021You Only Learn One Representation: Unified Network for Multiple Taskscited by 86
  6. 2021DETReg: Unsupervised Pretraining with Region Priors for Object Detectioncited by 76
  7. 2018Conditional Affordance Learning for Driving in Urban Environmentscited by 70
  8. 2020A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detectioncited by 70
  9. 2020MobileDets: Searching for Object Detection Architectures for Mobile Acceleratorscited by 55
  10. 2021Instance-Conditional Knowledge Distillation for Object Detectioncited by 42
  11. 2019Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Cloudscited by 35
  12. 2019SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systemscited by 26
  13. 2017LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systemscited by 7
  14. 20193D Bounding Box Estimation for Autonomous Vehicles by Cascaded Geometric Constraints and Depurated 2D Detections Using 3D Resultscited by 7
  15. 2019Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillationcited by 6
  16. 2021NAS-FCOS: Efficient Search for Object Detection Architecturescited by 6
  17. 2021Small Object Detection Based on Modified FSSD and Model Compressioncited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 7; 2025: 2.

14 papers.

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  1. 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 76
  2. 2023Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detectioncited by 14
  3. 2024Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Trackingcited by 8
  4. 2024LLM-Optic: Unveiling the Capabilities of Large Language Models for Universal Visual Groundingcited by 7
  5. 2023A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networkscited by 6
  6. 2024Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodologycited by 6
  7. 2024A Comprehensive Evaluation of Deep Learning Object Detection Models on Heterogeneous Edge Devicescited by 6
  8. 2022Robust Perception Architecture Design for Automotive Cyber-Physical Systemscited by 4
  9. 2024Engineering an Efficient Object Tracker for Non-Linear Motioncited by 4
  10. 2024Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarkscited by 4
  11. 2024Evaluating the Adversarial Robustness of Detection Transformerscited by 1
  12. 2023Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability Connectioncited by 0
2 more
  1. 2025UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehiclescited by 0
  2. 2025How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real Worldcited by 0