# 4C: A Computation, Communication, and Control Co-Design Framework for CAVs

arXiv: https://arxiv.org/abs/2107.01142 · HTML: https://mlsanity.com/paper/2107.01142/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 2 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead](https://mlsanity.com/paper/2005.10027/) (2020)
- [The edge cloud: A holistic view of communication, computation and caching](https://mlsanity.com/paper/1802.00700/) (2018)

## Abstract (arXiv)

Connected and autonomous vehicles (CAVs) are promising due to their potential safety and efficiency benefits and have attracted massive investment and interest from government agencies, industry, and academia. With more computing and communication resources are available, both vehicles and edge servers are equipped with a set of camera-based vision sensors, also known as Visual IoT (V-IoT) techniques, for sensing and perception. Tremendous efforts have been made for achieving programmable communication, computation, and control. However, they are conducted mainly in the silo mode, limiting the responsiveness and efficiency of handling challenging scenarios in the real world. To improve the end-to-end performance, we envision that future CAVs require the co-design of communication, computation, and control. This paper presents our vision of the end-to-end design principle for CAVs, called 4C, which extends the V-IoT system by providing a unified communication, computation, and control co-design framework. With programmable communications, fine-grained heterogeneous computation, and efficient vehicle controls in 4C, CAVs can handle critical scenarios and achieve energy-efficient autonomous driving. Finally, we present several challenges to achieving the vision of the 4C framework.