Aggregation of Direct and Indirect Positioning Sensors for Vehicle Guidance

Advanced Vehicle Control Systems (AVCS) require large numbers of sensors for different levels within the control hierarchy. Whereas all sensors contain uncertainty to some degree, different sensors are particularly useful for specific environmental conditions. Therefore, sensor redundancy is essential to achieving high sensor data fidelity for use in real-world, non-ideal, unpredictable environments. In this work, a positioning sensor system, which includes Global Positioning System (GPS) receivers, a radar sensor, and a linear transducer is investigated. Positioning sensors provide information about the absolute or relative position of vehicles, a crucial component of the control system. GPS is potentially powerful for AVCS because of its high accuracy achievable by Differential GPS (DGPS) and other advanced GPS techniques. Field tests using these three sensors have been performed in cooperation with SRI International. Sensor noise models of GPS, radar, and linear Transducer sensors are developed based on the test data. A synchronization method is suggested for the scenario in which sensors output data at different frequencies and time delays. Two types of validation and fusion algorithms, PDAF and FUSVAF, are implemented for the open loop test data and the results are compared. A closed loop simulation has been performed using a simple PDD controller as the follower control law within a platoon. The sensor models developed here are applied in the simulation, and the two fusion algorithms are implemented and the results are compared. Finally, additional simulations incorporate results into VDL. Key words: sensor modeling, sensor validation, sensor fusion, data fusion, data synchronization, supervisory control, VDL, management of uncertainty, reliability