Automotive Cloud Computing

Abstract:

With today’s intelligent vehicles, there are a variety of information-rich sensors, both on and off-board, that can stream data to assist drivers. In the future, we imagine physical infrastructure capable of sensing and communicating data to vehicles to improve a driver’s awareness on the road. To process this data and present information to the driver in real-time, in this talk, we introduce a real-time vehicle/cloud coordination system and a couple of novel online scheduling algorithms designed to exploit distributed devices that are connected via wireless links.

It is of our interest to find optimal assignments of tasks to local and remote devices that can take into account the application-specific profile, availability of computational resources, and link connectivity, and find a balance between energy consumption costs of mobile devices and latency for delay-sensitive applications. First, we formulate an NP-hard problem to minimize the application latency while meeting prescribed resource utilization constraints and propose a novel fully polynomial time approximation scheme (FPTAS) to solve it. Later, we further enhance and formulate the task assignment problem as an online learning problem using an adversarial multi-armed bandit framework. We propose MABSTA, a novel algorithm that learns the performance of unknown devices and channel qualities continually through exploratory probing and makes task assignment decisions by exploiting the gained knowledge.

Using two example real-world applications – Simultaneous Localization and Mapping (SLAM) and collaborative perception enhancement, we explore how these applications can instead leverage cloud servers and edge servers, utilizing their inexpensive and elastic resource pool to seamlessly augment vehicle onboard computing capability. We also illustrate the critical architecture changes on both vehicle side and cloud side.

Biography:

Dr. Fan Bai is a staff researcher and lab group manager in the Electrical & Control Systems Lab., Research & Development and Planning, General Motors Corporation. Before joining General Motors research lab, he received the B.S. degree in automation engineering from Tsinghua University, Beijing, China, in 1999, and the M.S.E.E. and Ph.D. degrees in electrical engineering, from University of Southern California, Los Angeles, in 2005.

His current research is focused on the discovery of fundamental principles and the analysis and design of protocols/systems for next-generation vehicular networks, for safety, telematics and infotainment applications. He published about 100 research papers in top-quality conferences and journals and received more than 14,000 citations (according to Google Scholar). He also has more than 120 patents granted or pending.

He received Charles L. McCuen Special Achievement Award from General Motors Corporation in recognition of his accomplishment in area of vehicle-to-vehicle communications for drive assistance & safety. He was featured as “ITS People” in 2014 by IEEE ITS Magazine for his technical contributions to vehicular networks and intelligent transportation systems. He serves as Technical Program Co-Chairs for IEEE WiVec 2007, IEEE MoVeNet 2008, ACM VANET 2011 and ACM VANET 2012, among other leading roles in academic and industry technical conferences. He is an Associate Editor of IEEE Transaction on Vehicular Technology and IEEE Transaction on Mobile Computing, and he also serves as guest editors for IEEE Journal of Selected Areas in Communications, IEEE Wireless Communication Magazine, IEEE Vehicular Technology Magazine and Elsevier AdHoc Networks Journal. He is a Fellow and Distinguished Lecturer of IEEE.