Overall Research Project Description One of the promises of autonomous or AI-aided vehicular networks is that transportation tasks will become more efficient and safer. The enabling technologies for autonomous driving are a combination of sophisticated sensors for perception, powerful classification algorithms, and model-based predictive control systems. Despite their increasing popularity, autonomous vehicles remain a niche accessible to only a few, and widespread adoption of this technology is still many years away. The coexistence of autonomous driving and human operations creates a complex socio-technical system, presenting challenges related to both technical safety concerns and potential ethical and regulatory issues.

While a full autonomy control loop may not be available to most drivers, a significant portion of vehicles in the American market today are equipped with various levels of sensors to perceive their surroundings and possess some computing capabilities. Given that mobile phones are widely adopted, it is reasonable to assume that a communication module is present in the majority of vehicles. By targeting autonomous or semi-autonomous vehicles through the assessment of computation capability, this project addresses the challenges of safety and efficient transportation by facilitating resource sharing at micro and macro cloud infrastructure levels. In this project, the students will work on the development of a zero-touch vehicular network infrastructure to share sensorial data available in conventional vehicles.