# Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies

arXiv: https://arxiv.org/abs/2006.06091 · HTML: https://mlsanity.com/paper/2006.06091/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 30 of 41)

- [Deep Deterministic Uncertainty: A Simple Baseline](https://mlsanity.com/paper/2102.11582/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation](https://mlsanity.com/paper/2306.04344/) (2023)
- [Tensor Program Optimization with Probabilistic Programs](https://mlsanity.com/paper/2205.13603/) (2022)
- [Deep Deterministic Uncertainty for Semantic Segmentation](https://mlsanity.com/paper/2111.00079/) (2021)
- [Mitigating Transformer Overconfidence via Lipschitz Regularization](https://mlsanity.com/paper/2306.06849/) (2023)
- [Applications of Large Scale Foundation Models for Autonomous Driving](https://mlsanity.com/paper/2311.12144/) (2023)
- [Neural Architecture Search for Dense Prediction Tasks in Computer Vision](https://mlsanity.com/paper/2202.07242/) (2022)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks](https://mlsanity.com/paper/2404.03099/) (2024)
- [Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4](https://mlsanity.com/paper/2203.06649/) (2022)
- [LiDAR-as-Camera for End-to-End Driving](https://mlsanity.com/paper/2206.15170/) (2022)
- [Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions](https://mlsanity.com/paper/2401.04305/) (2024)
- [A framework for benchmarking uncertainty in deep regression](https://mlsanity.com/paper/2109.09048/) (2021)
- [Visualizing the Diversity of Representations Learned by Bayesian Neural Networks](https://mlsanity.com/paper/2201.10859/) (2022)
- [Bimodal Distributed Binarized Neural Networks](https://mlsanity.com/paper/2204.02004/) (2022)
- [Exploiting T-norms for Deep Learning in Autonomous Driving](https://mlsanity.com/paper/2402.11362/) (2024)
- [End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing](https://mlsanity.com/paper/2309.00296/) (2023)
- [Mapping the Mind of an Instruction-based Image Editing using SMILE](https://mlsanity.com/paper/2412.16277/) (2024)
- [A Survey on Datasets for Decision-making of Autonomous Vehicle](https://mlsanity.com/paper/2306.16784/) (2023)
- [PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers](https://mlsanity.com/paper/2307.00313/) (2023)
- [Combating the effects of speed and delays in end-to-end self-driving](https://mlsanity.com/paper/2312.06670/) (2023)
- [Forward Learning for Gradient-based Black-box Saliency Map Generation](https://mlsanity.com/paper/2403.15603/) (2024)
- [From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving](https://mlsanity.com/paper/2505.22067/) (2025)
- [Why Calibration Error is Wrong Given Model Uncertainty: Using Posterior Predictive Checks with Deep Learning](https://mlsanity.com/paper/2112.01477/) (2021)
- [On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Networks](https://mlsanity.com/paper/2207.03400/) (2022)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network](https://mlsanity.com/paper/2404.10263/) (2024)
- [LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks](https://mlsanity.com/paper/2412.03993/) (2024)
- [Application of Multimodal Large Language Models in Autonomous Driving](https://mlsanity.com/paper/2412.16410/) (2024)

## Abstract (arXiv)

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. This is a survey of autonomous driving technologies with deep learning methods. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task level respectively, behavior modelling and prediction of vehicle driving and pedestrian trajectories.