US 12,275,420 B2
System and method for minimizing aliasing in sample-based estimators
Christopher Monaco, Sunnyvale, CA (US)
Assigned to Mercedes-Benz Group AG, Stuttgart (DE)
Filed by Mercedes-Benz Group AG, Stuttgart (DE)
Filed on Jan. 24, 2023, as Appl. No. 18/100,769.
Prior Publication US 2024/0246550 A1, Jul. 25, 2024
Int. Cl. B60W 50/06 (2006.01); B60W 10/18 (2012.01); B60W 10/20 (2006.01); B60W 60/00 (2020.01); G01C 21/28 (2006.01); G07C 5/02 (2006.01)
CPC B60W 50/06 (2013.01) [B60W 10/18 (2013.01); B60W 10/20 (2013.01); B60W 60/001 (2020.02); G01C 21/28 (2013.01); G07C 5/02 (2013.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); B60W 2555/20 (2020.02)] 15 Claims
OG exemplary drawing
 
1. A computing system of an autonomous vehicle, the computing system comprising:
one or more processors;
a memory storing instructions that, when executed by the one or more processors, cause the computing system to:
receive sensor data from one or more sensors of the autonomous vehicle, the sensor data corresponding to a real-time sensor view of a surrounding environment of the autonomous vehicle;
dynamically execute a sample-based estimator applying the Nyquist-Shannon sampling theorem on the sensor data to dynamically generate a state estimate, the sample-based estimator optimizing a probability distribution dilation of the sensor data based on a minimum measurement uncertainty value to minimize aliasing in the sample-based estimator; and
autonomously operate a set of control mechanisms of the autonomous vehicle to drive the autonomous vehicle along a road segment based, at least in part, on the dynamically generated state estimate from the sample-based estimator.