CPC B60W 50/085 (2013.01) [G06N 20/00 (2019.01); B60W 2050/0018 (2013.01); B60W 2050/0082 (2013.01)] | 19 Claims |
1. A method comprising:
assigning, using at least one processor, a set of initial physical states to a set of objects for a set of simulated driving scenarios;
generating, using the at least one processor, the set of simulated driving scenarios using the initial physical states of the objects in the set of objects;
determining, using the at least one processor, one or more failed driving scenarios in the set of simulated driving scenarios;
excluding, using the at least one processor, the one or more failed driving scenarios from the set of simulated driving scenarios, wherein the one or more failed driving scenarios comprise a driving scenario where acceleration or deceleration values of one or more objects in the set of objects are higher or lower than one or more specified threshold values;
training, using the at least one processor, a machine learning model using first samples of the set of simulated driving scenarios;
obtaining, using the at least one processor, tuning data comprising second samples of the set of simulated driving scenarios;
tuning, using the at least one processor, the trained machine learning model with the tuning data;
optimizing, using the at least one processor, a loss function of predictions output by the tuned machine learning model; and
causing, using the at least one processor, a vehicle to operate in an environment using the tuned machine learning model.
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