A Quest for Human-Robot Cohabitation in the Age of Self Driving Automobiles
With recent advances in imaging sensors, embedded computing, machine perception, learning, planning and control, intelligent vehicle technology is moving tantalizingly closer to a future with large- scale deployment of self-driving automobiles on roadways. However, we are also realizing that many important issues need deeper examination so that the safety, reliability and robustness of these highly complex systems can be assured. Toward this end, we highlight research issues as they relate to the understanding of human agents interacting with the automated vehicle, who are either occupants of such vehicles, or who are in the near vicinity of the vehicles. The main idea is to develop an approach to properly design, implement and evaluate methods and computational frameworks for distributed systems where intelligent robots and humans cohabit, with proper understanding of mutual goals, plans, intentions, risks and safety parameters. We emphasize the need and the implications of utilizing a holistic approach, where driving in a naturalistic context is observed over long periods to learn behaviors of human agents in order to predict intentions and interactivity patterns of all intelligent agents. Development of highly automated vehicles opens new research avenues in machine learning, modeling, active control, perception of dynamic events, and novel architectures for distributed cognitive systems. This presentation will give examples of some of the accomplishments in the design of such systems and also highlight important research challenges yet to be overcome.
Mohan Manubhai Trivedi is a Distinguished Professor of Electrical and Computer Engineering and founding director of the Computer Vision and Robotics Research Laboratory, as well as the Laboratory for Intelligent and Safe Automobiles (LISA) at the University of California San Diego. Trivedi’s team has played a key role in several major research collaborative initiatives. These include design, development and deployment of distributed video arrays for wide area activity analysis, privacy preserving filters for surveillance video arrays for transportation infrastructures including for freeways, international bridges, and stadiums; systems for vehicle collision avoidance, pedestrian protection and intent analysis, lane-change/turn/merge assistance; vision-based systems for “smart” airbags, redictive driver intent and activity analysis systems; and panoramic-view surround safety systems and autonomous robotic teams for railway track maintenance and for hazardous environments. His team is recognized as the most prolific and most cited in the intelligent vehicles and intelligent transportations systems field. He has won over 20 “Best/Finalist” Paper award, has received the IEEE ITS Society’s Outstanding Research Award and LEAD Institution Award as well as the Meritorious Service and Pioneer Award (Technical Activities) of the IEEE Computer Society. He has given over 100 keynote/plenary talks and he regularly serves on panels dealing with technological, strategic, privacy, and ethical issues surrounding research areas he is involved in. He is a Fellow of IEEE, SPIE, and IAPR. Trivedi has served as the Robotics Technical Committee Chair for the IEEE Computer Society, on the Governing Boards of the IEEE Systems, Man & Cybernetics and ITSC Society, Editor-in-Chief of the Machine Vision Applications journal and charter member/vicechair of the University of California System wide Digital Media Innovation (UC Discovery) program. Trivedi serves regularly as a consultant to industry and government agencies in the USA and abroad.