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

arXiv: https://arxiv.org/abs/2406.00490 · HTML: https://mlsanity.com/paper/2406.00490/

## Key facts

- Cited by 9 papers in the map (data through 14 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.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 21 of 21)

- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Visual In-Context Learning for Large Vision-Language Models](https://mlsanity.com/paper/2402.11574/) (2024)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Pareto Self-Supervised Training for Few-Shot Learning](https://mlsanity.com/paper/2104.07841/) (2021)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition](https://mlsanity.com/paper/2303.12512/) (2023)
- [StegaNeRF: Embedding Invisible Information within Neural Radiance Fields](https://mlsanity.com/paper/2212.01602/) (2022)
- [Accelerating Parallel Sampling of Diffusion Models](https://mlsanity.com/paper/2402.09970/) (2024)
- [3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2308.10123/) (2023)
- [Residual-based Language Models are Free Boosters for Biomedical Imaging](https://mlsanity.com/paper/2403.17343/) (2024)
- [Segment Every Out-of-Distribution Object](https://mlsanity.com/paper/2311.16516/) (2023)
- [TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection](https://mlsanity.com/paper/2403.08108/) (2024)
- [Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task](https://mlsanity.com/paper/2405.03026/) (2024)
- [DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks](https://mlsanity.com/paper/2210.05370/) (2022)
- [z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning](https://mlsanity.com/paper/2302.02589/) (2023)
- [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://mlsanity.com/paper/2308.00247/) (2023)
- [Vehicle Pose and Shape Estimation through Multiple Monocular Vision](https://mlsanity.com/paper/1802.03515/) (2018)
- [Research on Image Recognition Technology Based on Multimodal Deep Learning](https://mlsanity.com/paper/2405.03091/) (2024)
- [Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding](https://mlsanity.com/paper/2405.13381/) (2024)
- [Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge](https://mlsanity.com/paper/2302.09221/) (2023)

## Built on by (most cited first, 9 of 9)

- [Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated](https://mlsanity.com/paper/2406.04658/) (2024)
- [Application of Natural Language Processing in Financial Risk Detection](https://mlsanity.com/paper/2406.09765/) (2024)
- [MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detection](https://mlsanity.com/paper/2408.00438/) (2024)
- [Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual Defect Detection](https://mlsanity.com/paper/2501.09187/) (2025)
- [Deep Learning-Based Multi-Modal Fusion for Robust Robot Perception and Navigation](https://mlsanity.com/paper/2504.19002/) (2025)
- [Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems](https://mlsanity.com/paper/2501.01557/) (2025)
- [Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs](https://mlsanity.com/paper/2505.01742/) (2025)
- [Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation](https://mlsanity.com/paper/2505.10522/) (2025)
- [Defense That Attacks: How Robust Models Become Better Attackers](https://mlsanity.com/paper/2512.02830/) (2025)

## 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.