US 12,271,193 B2
Architecture and methodology for defensive autonomous driving using a probabilistic control strategy
Reza Hajiloo, Waterloo (CA); Seyedalireza Kasaiezadeh Mahabadi, Novi, MI (US); Ehsan Asadi, Markham (CA); Gianmarc Coppola, Richmond Hill (CA); and Bakhtiar B. Litkouhi, Washington, MI (US)
Assigned to GM GLOBAL TECHNOLOGY OPERATIONS LLC, Detroit, MI (US)
Filed by GM Global Technology Operations LLC, Detroit, MI (US)
Filed on Jan. 9, 2023, as Appl. No. 18/151,608.
Prior Publication US 2024/0231358 A1, Jul. 11, 2024
Int. Cl. G05D 1/00 (2024.01); G05D 1/02 (2020.01); B60W 60/00 (2020.01); G01C 21/00 (2006.01); G01C 21/34 (2006.01)
CPC G05D 1/0088 (2013.01) [G05D 1/0212 (2013.01); B60W 60/001 (2020.02); B60W 60/0011 (2020.02); B60W 60/0027 (2020.02); G01C 21/005 (2013.01); G01C 21/34 (2013.01); G01C 21/3453 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A method for controlling an autonomous vehicle, comprising
receiving road data, wherein the road data includes information about a road ahead of the autonomous vehicle and is at least partly received from a plurality of sensors of the autonomous vehicle, and the road data includes information about a plurality of potential events along the road ahead of the autonomous vehicle;
detecting the plurality of potential events along the road ahead of the autonomous vehicle using the plurality of sensors of the autonomous vehicle;
determining, in real time, a probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur while the autonomous vehicle moves along the road, wherein the probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur is a function of a reliability of each of the plurality of sensors that detected the plurality of potential events along the road ahead of the autonomous vehicle, wherein a probabilistic predictive control includes a vehicle prediction model and an optimizer, wherein the vehicle prediction model uses a planar bicycle model, wherein the optimizer uses an objective function to optimize a plurality of control actions, and wherein the optimizer is constrained by control action constraints, control action slew rate constraints, yaw rate stability constraints, sideslip stability constraints, and lateral deviations constraints;
determining, in real time, an adjusted planned path using the probabilistic predictive control that takes into account the probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur;
determining the plurality of control actions using the probabilistic predictive control; and
controlling the autonomous vehicle to cause the autonomous vehicle to autonomously follow the adjusted planned path using the plurality of control actions.