The field of CAV stands at the confluence of three evolving disciplines – evolving sensors Internet of Things (IoT) technology, emerging standards for connectivity of vehicles, and growing applications of AI/Machine learning to wireless networking. The number of connected IoT devices are likely to grow from 9.5 billion devices in 2019 to 22.5 billion devices or more by 2025. More optimistic estimates project the number of IoT devices in 2025 to be 55 billion connected devices. Fueling the growth in the evolution of vehicles towards total automation is the development of novel sensors, 3D cameras, lidars and radars and their ability to connect to the Internet, and upload data to a cloud. The sensors of an autonomous vehicle collect anywhere from 1.4 TB to 19 TB of data per hour. Consequently, applications of IoT devices and sensors have rapidly expanded to integrate intelligent sensing and processing along with smart applications of the technology into various fields such as smart homes, smart appliances, enterprises, smart transportation including CAV, smart cities, agriculture, energy, security, healthcare, shopping, location-based services including tracking and other similar fields.

The vast amount of raw data collected must by mined for it to become useful in ensuring traffic safety by means such as intelligent rerouting of traffic or distribution of information on roadwork activities or accidents. Machine learning is a mechanism that has become extremely powerful in extracting meaningful data. A number machine learning algorithms exist and can be broadly classified under unsupervised, supervised, and reinforcement learning algorithms. A number of algorithms exist under each category.

With the advent of 5G and the next generation wireless technologies of 6G and beyond, artificial intelligence and machine learning will play a significant role at all levels of the protocol stack and in creating novel applications. This Workshop will address the impact of machine learning and their applications to CAV with several use cases.