Current production cruise control systems maintain a set speed of a vehicle with no information about other vehicles. Adaptive Cruise Control (ACC) Systems use sensor information to monitor vehicles ahead, and change cruise speed accordingly.
Proposed research is to develop a more robust and reliable ACC by adjusting its performance to the driving conditions. This entails an appropriate combination of sensory data, physically based dynamic models, and logical situation models in an intelligent data fusion framework. Present work involves the application of the Interacting Multiple Model (IMM) approach to target tracking. By incorporating a finite number of possible kinematic models, IMM can predict the future motion of a target. Thereby, the ACC-equipped vehicle can make "intelligent" decisions based on the predicted motion of the preceding vehicles.
