# Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

arXiv: https://arxiv.org/abs/2305.11239 · HTML: https://mlsanity.com/paper/2305.11239/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 172 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 30 of 31)

- [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?](https://mlsanity.com/paper/1610.01983/) (2016)
- [Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review](https://mlsanity.com/paper/2005.09830/) (2020)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review](https://mlsanity.com/paper/2004.05224/) (2020)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers](https://mlsanity.com/paper/1804.07871/) (2018)
- [Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques](https://mlsanity.com/paper/1605.04965/) (2016)
- [InstaFormer: Instance-Aware Image-to-Image Translation with Transformer](https://mlsanity.com/paper/2203.16248/) (2022)
- [Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches](https://mlsanity.com/paper/2102.05897/) (2021)
- [Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology](https://mlsanity.com/paper/1905.03419/) (2019)
- [Autonomous Braking System via Deep Reinforcement Learning](https://mlsanity.com/paper/1702.02302/) (2017)
- [End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies](https://mlsanity.com/paper/1710.03804/) (2017)
- [Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation](https://mlsanity.com/paper/2111.07971/) (2021)
- [A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Development](https://mlsanity.com/paper/2002.08736/) (2020)
- [Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies](https://mlsanity.com/paper/1905.03428/) (2019)
- [A Game-Theoretic Framework for Autonomous Vehicles Velocity Control: Bridging Microscopic Differential Games and Macroscopic Mean Field Games](https://mlsanity.com/paper/1903.06053/) (2019)
- [A Survey on Resource Allocation in Vehicular Networks](https://mlsanity.com/paper/1909.13587/) (2019)
- [Securing Vehicle-to-Everything (V2X) Communication Platforms](https://mlsanity.com/paper/2003.07191/) (2020)
- [Control Strategies for Autonomous Vehicles](https://mlsanity.com/paper/2011.08729/) (2020)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Optimal Control for Speed Harmonization of Automated Vehicles](https://mlsanity.com/paper/1611.04647/) (2016)
- [Evaluation of Lane Departure Correction Systems Using a Stochastic Driver Model](https://mlsanity.com/paper/1702.05779/) (2017)
- [Radar networks: A review of features and challenges](https://mlsanity.com/paper/2003.12746/) (2020)
- [Applications of Game Theory in Vehicular Networks: A Survey](https://mlsanity.com/paper/2103.11874/) (2021)
- [Map matching when the map is wrong: Efficient vehicle tracking on- and off-road for map learning](https://mlsanity.com/paper/1809.09755/) (2018)

## Built on by (most cited first, 25 of 25)

- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Drive Like a Human: Rethinking Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2307.07162/) (2023)
- [DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving](https://mlsanity.com/paper/2408.00415/) (2024)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2407.14239/) (2024)
- [InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.13047/) (2025)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Driving](https://mlsanity.com/paper/2412.18511/) (2024)
- [FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion](https://mlsanity.com/paper/2308.06931/) (2023)
- [Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2403.18209/) (2024)
- [A Survey on Large Language Model-empowered Autonomous Driving](https://mlsanity.com/paper/2409.14165/) (2024)
- [Vision-Centric 4D Occupancy Forecasting and Planning via Implicit Residual World Models](https://mlsanity.com/paper/2510.16729/) (2025)
- [VLM-UDMC: VLM-Enhanced Unified Decision-Making and Motion Control for Urban Autonomous Driving](https://mlsanity.com/paper/2507.15266/) (2025)
- [AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery](https://mlsanity.com/paper/2309.16706/) (2023)
- [Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervision](https://mlsanity.com/paper/2310.05202/) (2023)
- [Compositionally Safe Construction of Autonomous Driving Systems](https://mlsanity.com/paper/2405.11995/) (2024)
- [Maps for Autonomous Driving: Full-process Survey and Frontiers](https://mlsanity.com/paper/2509.12632/) (2025)
- [Scenarios Engineering driven Autonomous Transportation in Open-Pit Mines](https://mlsanity.com/paper/2405.00690/) (2024)
- [On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance](https://mlsanity.com/paper/2411.17152/) (2024)
- [Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control](https://mlsanity.com/paper/2502.15262/) (2025)
- [Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions](https://mlsanity.com/paper/2606.09568/) (2026)
- [AI Control Scientist: LLM-driven Agentic System for Automated Control Design](https://mlsanity.com/paper/2608.26780/) (2026)
- [A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform](https://mlsanity.com/paper/2610.00926/) (2026)

## Abstract (arXiv)

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.