AI in vision systems and imaging, decision making, autonomous command and control, prediction of dynamic traffic and road conditions, vehicle design, routing, scheduling and maintenance, among others leading to improved safety, efficiency, and decision making throughout transportation systems.
Modern growing, dynamic, global transportation systems benefit from many uses of AI. Latest advances in vision systems alone have led to rapid development in autonomous surface- and air-based vehicles, traffic and pedestrian detection and avoidance, automated license plate recognition, and improved means to monitor driver performance and safety. At larger scales AI can be used to understand and predict traffic flows, to help re-distribute flows in optimal ways in light of incidents that may arise, help with the monitoring of road conditions, etc. “Smart cities” are now embedding these technologies in the transportation grids to improve the safety of everyone using the system. AI has the opportunity of revolutionizing the transportation of goods through autonomous trucks, ships, and aircraft. Within aerospace, AI has additional utility from the design optimization of new aircraft to the development of autonomous air taxis, to the optimized routing of aerial vehicles in crowded urban environments, to long-endurance space missions where AI is required to perform smart decision-making in real-time perhaps even without the ability for pre-confirmation from Earth-bound human controllers. AI can also play an important role in prognostics for maintenance of jet turbines, for the optimal routing of vehicles with simultaneous sensor allocation, and so forth.
Eric Bechhoefer (Green Power Monitoring Systems)
Mariagrazia Dotoli (Politecnico di Bari, Italy)
Gary Fogel (Natural Selection, Inc.)
Nikunj C. Oza (NASA Ames)
Karen Panetta (Tufts University)
Christopher Silva (NASA Ames)
Adrian Stoica (NASA JPL)
Liang Tang (General Electric)
Kanishka Tyagi (Aptiv)
Huafeng Yu (Boeing)