Data-driven AV is nothing new. For the past five years, AV endpoints have been migrating to the network with the promise of capturing room metrics and system analytics. The cloud, 5G's lightning-fast connectivity, IoT sensors, and artificial intelligence (AI) are converging to capture more valuable data in more locations. Some AV professionals believe this confluence will usher in a new era of design, one in which systems are built with learned user behaviors in mind. AI is already making an impact in higher education.

A synthesisof methods from cybernetics and AI yields a concept of intelligence for autonomous mobile systems that integrates closed-loop visual perception and goal-oriented action cycles using spatiotemporal models. In a layered architecture, systems dynamics methods with differential models prevail on the lower, data-intensive levels, but on higher levels, AI-type methods are used. Knowledge about the world is geared to classes of objects and subjects. Subjects are defined as objects with additional capabilities of sensing, data processing, decision making, and control application. Specialist processes for visual detection and efficient tracking of class members have been developed. On the upper levels, individual instantiations of these class members are analyzed jointly in the task context, yielding the situation for decision making. As an application, vertebrate-type vision for tasks in vehicle guidance in naturally perturbed environments was investigated with a distributed PC system. Experimental results with the test vehicle VAMORS are discussed.