# A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

arXiv: https://arxiv.org/abs/2303.05665 · HTML: https://mlsanity.com/paper/2303.05665/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2023.

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

- [WcDT: World-centric Diffusion Transformer for Traffic Scene Generation](https://mlsanity.com/paper/2404.02082/) (2024)
- [FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems](https://mlsanity.com/paper/2304.01534/) (2023)
- [A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles](https://mlsanity.com/paper/2409.13783/) (2024)
- [Unified End-to-End V2X Cooperative Autonomous Driving](https://mlsanity.com/paper/2405.03971/) (2024)
- [Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses](https://mlsanity.com/paper/2502.04201/) (2025)
- [Safe and Efficient CAV Lane Changing using Decentralised Safety Shields](https://mlsanity.com/paper/2505.01453/) (2025)
- [Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning](https://mlsanity.com/paper/2308.14602/) (2023)
- [Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning](https://mlsanity.com/paper/2405.12228/) (2024)
- [Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control](https://mlsanity.com/paper/2502.15262/) (2025)

## Abstract (arXiv)

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.