There have been rapid advances in control and automated driving aids in today's cars, with a concomitant rise in the breadth and complexity of driver interaction. Thus there is a need for a clear, consistent, logical, and holistic design methodology to the design and analysis of the driver-vehicle interaction environment. Such design method should also take into account expected future technological enhancements and advances. In this paper we present some emerging automated driving aids that are currently implemented in modern vehicles and those that are anticipated in the coming years. We focus on three automation features -- adaptive cruise control (ACC), Lane Centering (LC), and Full Speed Range ACC (FSRA). The design methodology is based on formal modeling of the functionality and user interaction of these systems and analysis of their corresponding user interfaces. The approach demonstrated here is valid for a wide range of user interaction systems where operational complexity requires careful and verifiable design.

In this paper we discuss a formal approach to the design and analysis of automotive systems, from a human-machine interaction (HMI) point of view. Specifically, we detail the behavior of a generic climate control system, present a statecharts model of this system, and discuss aspects of user interaction analysis. Several general principles for the design of climate control systems are illustrated and discussed. The topic of design patterns, in the context of a formal description of user interaction, is introduced, and two design patterns are illustrated and discussed.

The traditional framework for discrete-event control is extended to include the case of control with active events, in which both the user and the environment have events that they can trigger. A variety of liveness and safety specifications can be considered within this extended framework. A synthesis algorithm of minimally restrictive controllers is outlined.

The authors describe an algorithm for navigating a polygonal robot, capable of translational motion, in an unknown environment. The environment contains stationary polygonal obstacles and is bounded by polygonal walls, all of which are initially unknown to the robot. The environment is learned during the navigation process by use of a laser range-finding device, and new knowledge is integrated with previously acquired information. A partial map of the environment, containing parts of the obstacles that were seen by the robot and the free space between them, is obtained. The obstacles in the map are transformed into a new set of expanded polygonal obstacles, allowing the robot to be treated as a point, and the navigation problem is reduced to point navigation among unknown polygonal obstacles. A navigation graph is built from the transformed obstacles and used to search for a piecewise linear path to the destination. The algorithm is proved to converge to the desired destination in a finite number of steps provided a path to the destination exists.>