MACHINE LEARNING MODELS OPERATING AT DIFFERENT FREQUENCIES FOR AUTONOMOUS VEHICLES
Systems and methods include machine learning models operating at different frequencies. An example method includes obtaining images at a threshold frequency from one or more image sensors positioned about a vehicle. Location information associated with objects classified in the images is determined based on the images. The images are analyzed via a first machine learning model at the threshold frequency. For a subset of the images, the first machine learning model uses output information from a second machine learning model, the second machine learning model being performed at less than the threshold frequency.
1 . (canceled)
2 . A system comprising:
one or more cameras;
one or more processors coupled to the one or more cameras, the one or more processors configured to:
implement a first trained machine learned model configured to:
receive first image data from the one or more cameras; and
based on the first image data and at a first time, detect new object data not previously detected by the system; and
implement a second trained machine learned model configured to:
receive second image data from the one or more cameras and prior object data previously detected by the first trained machine learned model; and
based on the second image data and the prior object data previously detected and at a time prior to the first time, determine a location for the prior object data.
3 . The system of claim 2 , wherein the second model is not trained to output new objects.
4 . The system of claim 2 , wherein the second image data includes the first image data.
5 . The system of claim 2 , wherein a portion of the second image data is not included in the first image data.
6 . The system of claim 2 , wherein the first model and the second model operate at different sampling rates.
7 . The system of claim 2 , wherein the second model is a tracker or a detector.
8 . The system of claim 2 , wherein the second model is further configured to determine a location based on received inertial measurement unit information or global satellite system information.
9 . A method implemented by a system of one or more processors, the method comprising:
receiving first image data from a one or more cameras;
detecting, by a first trained machine learned model, new object data not previously detected by the system, based on the first image data at a first time;
receiving second image data from the one or more cameras;
receiving prior object data previously detected by the first trained machine learned model; and
determining at a time prior to the first time, by a second machine learned model, a location for the prior object data based on the second image data and the prior object data.
10 . The method of claim 9 , wherein the second model is not trained to output new objects.
11 . The method of claim 9 , wherein the second image data includes the first image data.
12 . The method of claim 9 , wherein a portion of the second image data is not included in the first image data.
13 . The method of claim 9 , wherein the first model and the second model operate at different sampling rates.
14 . The method of claim 9 , wherein the second model is a tracker or a detector.
15 . The method of claim 9 , wherein the second model is further configured to determine a location based on received inertial measurement unit information or global satellite system information.
16 . Non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors to perform operations comprising:
receiving first image data from a one or more cameras;
detecting, by a first trained machine learned model, new object data not previously detected by the system, based on the first image data at a first time;
receiving second image data from the one or more cameras;
receiving prior object data previously detected by the first trained machine learned model; and
determining at a time prior to the first time, by a second machine learned model, a location for the prior object data based on the second image data and the prior object data.
17 . The computer-storage media of claim 16 , wherein the second model is not trained to output new objects.
18 . The computer-storage media of claim 16 , wherein the second image data includes the first image data.
19 . The computer-storage media of claim 16 , wherein a portion of the second image data is not included in the first image data.
20 . The computer-storage media of claim 16 , wherein the first model and the second model operate at different sampling rates.
21 . The computer-storage media of claim 16 , wherein the second model is a tracker or a detector.