A VLSI implementation of a neural car collision avoidance controller

The authors present a neural solution to the car collision avoidance problem. The complete path design from problem identification to a hardware implementation is discussed. It is shown that a thorough study of the control task leads to a well-chosen representation for the environment data (network input) and the control directives (network output) so that car dynamics are handled, and the learning and generalization capabilities of the neural network are fully exploited. The selection of a suitable network topology for the control problem is presented. The authors discuss the learning strategy and the construction of the learning set. After a functioning controller is considered, they discuss the mapping of the simulated network on a VLSI layout