Communications and Sensing: An Opportunity for Automotive Systems

Like many of you, I am still working remotely, due to COVID-19, while writing this editorial. As in the past two years, I was planning to give an update on the magazine from our editorial board meeting. However, since ICASSP was remote, we have not yet scheduled the board meeting. Instead, I have decided to talk about a topic of personal interest: connections between communications and sensing in the context of vehicular systems. I believe that this is an important signal processing topic that brings together researchers from different technical committees and societies. This editorial is also relevant given the special issue articles and feature article found in this issue.

In past IEEE Signal Processing Magazine (SPM) editorials, I have discussed vehicular applications of signal processing, going toward 6G cellular communications, and new opportunities in communications as realized during a pandemic. In each of these articles I hinted at potential opportunities related to communications and sensing (especially radar). These topics have been featured in other SPM content including a two-part series on advances in radar systems for modern civilian and commercial applications (issues 4 and 5 in 2019) and a two-part series on autonomous vehicles (this issue and the upcoming January 2021 issue). There is also alignment with the IEEE Signal Processing Society (SPS) Autonomous Systems Initiative. In this editorial, I expand on the potential of combining communications and sensing in different ways.

Vehicles are being equipped with more sensors to support higher levels of automation. These sensors form a network on the vehicle, whose information is fused for tasks like trajectory planning and obstacle avoidance. Perhaps even more interesting, though, is the combination of sensing and communications that turns a network of vehicles into a cooperative perception system. The sensor data from each vehicle can be exchanged and fused to create a more accurate picture of the environment, leveraging the multimodality of the sensors and their different perspectives. Imagine the contrast with sensor networking research from two decades ago [1], which envisioned networks of low-power, low-cost sensors with limited communication capability. The networks of sensor networks in a transportation system has vastly more capable sensors, highly advanced signal processing, significant computational resources, automation, and much more communication capability.