# End-to-end Autonomous Driving using Deep Learning: A Systematic Review

arXiv: https://arxiv.org/abs/2311.18636 · HTML: https://mlsanity.com/paper/2311.18636/

## Key facts

- Cited by 7 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 31 of its 40 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## 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, 30 of 31)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes](https://mlsanity.com/paper/2305.10430/) (2023)
- [Guided Conditional Diffusion for Controllable Traffic Simulation](https://mlsanity.com/paper/2210.17366/) (2022)
- [DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving](https://mlsanity.com/paper/2308.00398/) (2023)
- [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020)
- [Urban Driving with Conditional Imitation Learning](https://mlsanity.com/paper/1912.00177/) (2019)
- [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety](https://mlsanity.com/paper/1903.00640/) (2019)
- [HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps](https://mlsanity.com/paper/2106.14880/) (2021)
- [Variational End-to-End Navigation and Localization](https://mlsanity.com/paper/1811.10119/) (2018)
- [End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners](https://mlsanity.com/paper/1803.10158/) (2018)

## Built on by (most cited first, 7 of 7)

- [Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives](https://mlsanity.com/paper/2402.02968/) (2024)
- [VDT-Auto: End-to-end Autonomous Driving with VLM-Guided Diffusion Transformers](https://mlsanity.com/paper/2502.20108/) (2025)
- [CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine](https://mlsanity.com/paper/2509.15968/) (2025)
- [Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving](https://mlsanity.com/paper/2406.17265/) (2024)
- [Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering](https://mlsanity.com/paper/2409.12716/) (2024)
- [To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software](https://mlsanity.com/paper/2508.20892/) (2025)
- [End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems](https://mlsanity.com/paper/2610.01746/) (2026)

## Abstract (arXiv)

End-to-end autonomous driving is a fully differentiable machine learning system that takes raw sensor input data and other metadata as prior information and directly outputs the ego vehicle's control signals or planned trajectories. This paper attempts to systematically review all recent Machine Learning-based techniques to perform this end-to-end task, including, but not limited to, object detection, semantic scene understanding, object tracking, trajectory predictions, trajectory planning, vehicle control, social behavior, and communications. This paper focuses on recent fully differentiable end-to-end reinforcement learning and deep learning-based techniques. Our paper also builds taxonomies of the significant approaches by sub-grouping them and showcasing their research trends. Finally, this survey highlights the open challenges and points out possible future directions to enlighten further research on the topic.