CPC G06T 7/246 (2017.01) [B60W 60/001 (2020.02); G01C 21/3492 (2013.01); G01C 21/3691 (2013.01); G01S 19/42 (2013.01); G06F 18/214 (2023.01); G06T 7/292 (2017.01); G06V 20/584 (2022.01); G08G 1/0116 (2013.01); H04W 4/44 (2018.02); H04W 4/80 (2018.02); B60W 30/18154 (2013.01); B60W 2420/42 (2013.01); B60W 2420/52 (2013.01); B60W 2555/60 (2020.02); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01)] | 18 Claims |
1. A method comprising:
detecting, by one or more processors of a vehicle, a traffic light located at a first spatiotemporal location based on a first digital video stream captured by a first camera of the vehicle and a second digital video stream captured by a second camera of the vehicle;
determining, by the one or more processors, that the vehicle is located at a second spatiotemporal location, the determining comprising validating, by the one or more processors, first location data obtained from a plurality of sensors of the vehicle against second location data obtained by filtering, by a filter, the first location data;
determining, by the one or more processors, that the traffic light is expected at the first spatiotemporal location based on a semantic map referenced by the second spatiotemporal location;
responsive to determining that the traffic light is expected at the first spatiotemporal location, detecting, by the one or more processors, a traffic signal of the traffic light based on the first digital video stream and the second digital video stream, wherein detecting the traffic signal comprises:
extracting, by the one or more processors, a feature vector from the first digital video stream and the second digital video stream; and
determining, based on a machine learning model executed by the one or more processors, the traffic signal based on the feature vector, the machine learning model trained to recognize a color of an object based on features extracted from digital video streams of the object, wherein the machine learning model comprises a first artificial neural network trained to recognize red lights and a second artificial neural network trained to recognize green lights, the first artificial neural network independent of the second artificial neural network;
determining, by the one or more processors, a trajectory of the vehicle in accordance with the traffic signal; and
causing the vehicle to operate in accordance with the determined trajectory.
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