Optimization of Sensor, Bus, and Fusion Schedules of a Time-Triggered Sensor Fusion System

In this paper, we optimize a time-triggered, Kalman filter based, multi-sensor fusion system, used as an environmental perception platform for advanced driver assistance systems while satisfying constraints that are typical of a safety-related application. We argue that the overall system including effects from the sensor, bus, and fusion schedules as well as the treatment of measurements must be considered in order to optimize the fusion accuracy. Due to differences in measurement preprocessing data from sensors may not arrive in chronological order which requires special treatment for this out-of-sequence measurements (OOSM). As a result of this paper we identify regions in the scheduling parameter space that minimize the error covariance of the estimated states.