office:University of California at Berkeley
Due to the inherent noise of the sensor measurements, the latter are uncertain. Even if several sensors are used, it is not straight forward which sensor to trust or to what degree to trust it. If untreated, the quality of the ride suffers (as demonstrated in this animation) and in the worst case leads to unsafe conditions. Two different approaches for validation and fusion have been investigated: 1.) a probabilistic approach, and 2.) a fuzzy approach. The probabilistic approach is Kalman Filter based and assumes Gaussian noise distributions while the fuzzy approach makes no such assumptions. For fusion, the fuzzy approach uses validation gates which allow the assignment of confidence values to each sensor reading, depending on the sensor characteristics particular to each sensor.
Diagnosis of the sensors is performed on-line via fuzzy abductive methods. A ranking scheme calculates fuzzy closeness measures which are used to obtain the most likely failure (if any).
Below you can see validated and fused values for longitudinal distance as well as the original sensor values obtained from radar, sonar, and optical sensors.
Collaboration with other labs
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Check out the platooning movie (© PATH California):
Below you can see a few distance sensors which are mounted on the car