mlsanityData to 7 Oct 2026

Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology

arXiv 2406.00490 ↗First posted 1 Jun 2024cs.CV cs.AIcited by 9

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Application process in key areas. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future. potential.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 21 of its 36 references that are in the map. First posted on arXiv 1 Jun 2024.

Builds on

21 of 36 references.

References · 21

  1. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 401
  2. 2024Visual In-Context Learning for Large Vision-Language Modelscited by 124
  3. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  4. 2021Pareto Self-Supervised Training for Few-Shot Learningcited by 52
  5. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  6. 2020Computing Systems for Autonomous Driving: State-of-the-Art and Challengescited by 47
  7. 2022StegaNeRF: Embedding Invisible Information within Neural Radiance Fieldscited by 32
  8. 2023Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognitioncited by 32
  9. 2024Accelerating Parallel Sampling of Diffusion Modelscited by 30
  10. 20233D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimationcited by 28
  11. 2024Residual-based Language Models are Free Boosters for Biomedical Imagingcited by 23
  12. 2023Segment Every Out-of-Distribution Objectcited by 21
9 more
  1. 2024TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detectioncited by 21
  2. 2023z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learningcited by 17
  3. 2023Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Reviewcited by 17
  4. 2024Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Taskcited by 17
  5. 2022DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networkscited by 16
  6. 2018Vehicle Pose and Shape Estimation through Multiple Monocular Visioncited by 13
  7. 2024Research on Image Recognition Technology Based on Multimodal Deep Learningcited by 11
  8. 2024Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Biddingcited by 9
  9. 2023Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edgecited by 3

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 6.

9 papers.

  1. 2024Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integratedcited by 8
  2. 2024Application of Natural Language Processing in Financial Risk Detectioncited by 8
  3. 2024MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detectioncited by 1
  4. 2025Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual Defect Detectioncited by 1
  5. 2025Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigationcited by 1
  6. 2025Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systemscited by 0
  7. 2025Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTscited by 0
  8. 2025Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulationcited by 0
  9. 2025Defense That Attacks: How Robust Models Become Better Attackerscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023End-to-end Autonomous Driving using Deep Learning: A Systematic Review
  2. 2021Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment
  3. 2026Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning
  4. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey
  5. 2023One-Shot Learning of Visual Path Navigation for Autonomous Vehicles
  6. 2025Imitation Learning for Autonomous Driving: Insights from Real-World Testing
  7. 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
  8. 2022Tackling Real-World Autonomous Driving using Deep Reinforcement Learning