F2008-08-028
Sensor Fusion Approaches for High Precision Location Applications
A number of current and future driver assistant systems, such as navigation systems or communication based intersection assistance, are heavily reliant upon precise information as to the vehicle´s position on the ground. Detailed analysis of driving tasks has revealed that a number of future driver assistant systems will require accuracy to a resolution of at least 20cm with respect to vehicle position information.
The systems available to deliver position information, be they terrestrial, e.g. pseudolites, historical, e.g. odometry sensors, or satellite based, e.g. GPS, are not able to achieve the required level of accuracy. There is, therefore, a need to devise methods to increase the level of accuracy with which the position of the vehicle on the ground is measured.
Rather than rely on a single source of data in order to ascertain the vehicle´s position, we propose the fusion of data from several sensors, e.g. cameras, inertial sensors, GPS receivers, in order to produce a more accurate position vector. The means of combination is an extended Kalman filter.
The extended Kalman filter is described by a vector denoting its current state, for this application this is the estimated position vector, and an error covariance matrix which is a measure of the estimated accuracy of the state estimate, and for the purposes of the application presented here will describe the confidence with which the position vector produced can be trusted.
The ability to rate the confidence in the estimated position vector allows the driver to be aware of the extent of any degradation in the system´s performance should it occur. The costs of the system correlate with the level of accuracy it attains, hence, the confidence measure can be used to set the system parameters to fit the accuracy requirements of the task and thus keep costs down for applications where a high level of accuracy is less important.
The approach is further enhanced by adding a priori knowledge found in digital maps to the sensor data to be combined. Such knowledge comprises the elevation of the terrain, features such as roads, road signs, trees, etc., and any information about constraints (give-ways, traffic lights, one-way systems, etc).
Off the shelf sensor components were used to build the system which was installed in a standard production vehicle. The system design and an operational prototype are presented here along with the results of simulation based and real world tests.
Poster presentation: Vehicle safety