# Control of Connected and Automated Vehicles: State of the Art and Future Challenges

arXiv: https://arxiv.org/abs/1804.03757 · HTML: https://mlsanity.com/paper/1804.03757/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 11 of 11)

- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles](https://mlsanity.com/paper/2104.07830/) (2021)
- [Large Language Models for Human-like Autonomous Driving: A Survey](https://mlsanity.com/paper/2407.19280/) (2024)
- [A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways](https://mlsanity.com/paper/2109.11672/) (2021)
- [Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data](https://mlsanity.com/paper/2411.05842/) (2024)
- [CAV-AHDV-CAV: Mitigating Traffic Oscillations for CAVs through a Novel Car-Following Structure and Reinforcement Learning](https://mlsanity.com/paper/2407.02517/) (2024)
- [CorrA: Leveraging Large Language Models for Dynamic Obstacle Avoidance of Autonomous Vehicles](https://mlsanity.com/paper/2503.02076/) (2025)
- [Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning](https://mlsanity.com/paper/2111.13966/) (2021)
- [Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure](https://mlsanity.com/paper/2309.07504/) (2023)
- ["I Apologize For Not Understanding Your Policy": Exploring the Specification and Evaluation of User-Managed Access Control Policies by AI Virtual Assistants](https://mlsanity.com/paper/2505.07759/) (2025)
- [DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning](https://mlsanity.com/paper/2606.30694/) (2026)

## Abstract (arXiv)

Autonomous driving technology pledges safety, convenience, and energy efficiency. Challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.