US 11,993,288 B2
Automated cut-in identification and classification
Pablo Abad, Mountain View, CA (US); Aman Ved Kalia, Mountain View, CA (US); Juan Gomez-Ramos, Mountain View, CA (US); Jiayi Chen, Mountain View, CA (US); Jasman Singh Malik, Mountain View, CA (US); and Joe Riggs, Mountain View, CA (US)
Assigned to Waymo LLC, Mountain View, CA (US)
Filed by Waymo LLC, Mountain View, CA (US)
Filed on Dec. 29, 2021, as Appl. No. 17/565,232.
Prior Publication US 2023/0202530 A1, Jun. 29, 2023
Int. Cl. B60W 60/00 (2020.01); B60W 50/00 (2006.01); G05B 13/02 (2006.01)
CPC B60W 60/0027 (2020.02) [B60W 50/0098 (2013.01); G05B 13/0265 (2013.01); B60W 2050/0031 (2013.01); B60W 2520/10 (2013.01); B60W 2520/12 (2013.01); B60W 2554/4042 (2020.02); B60W 2554/4043 (2020.02); B60W 2554/4045 (2020.02); B60W 2554/4046 (2020.02); B60W 2554/801 (2020.02); B60W 2554/802 (2020.02); B60W 2710/18 (2013.01); B60W 2720/10 (2013.01); B60W 2720/12 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining operational data about one or more vehicles, wherein the operational data about a given vehicle of the one or more vehicles comprises information about sizes and locations over time of one or more additional vehicles in an environment of the given vehicle, information about a location and a trajectory of the given vehicle over time, and information about an extent of a lane being navigated by the given vehicle over time;
based on the operational data, identifying the presence of one or more cut-ins within the operational data, wherein identifying the presence of a given cut-in comprises: (i) determining a location and an extent over time of a bounding box surrounding a particular additional vehicle in the environment of the given vehicle, (ii) determining that the particular additional vehicle entered the lane being navigated by the given vehicle by at least one of determining that at least one vertex of the bounding box was located more than a threshold distance within the lane being navigated by the given vehicle or determining that the bounding box overlapped with the lane being navigated by the given vehicle by more than a threshold amount, and (iii) determining that the ability of the given vehicle to maintain its course and speed was impeded by the particular additional vehicle entering the lane being navigated by the given vehicle;
extracting, from the operational data, cut-in data that depicts one or more of the cut-ins identified within the operational data; and
based on the extracted cut-in data, training a model for controlling an autonomous vehicle, wherein training the model for controlling the autonomous vehicle based on the extracted cut-in data comprises:
determining one or more scores for the autonomous vehicle's reaction to one or more of the cut-ins identified within the operational data, wherein the autonomous vehicle's reaction to one or more of the cut-ins identified within the operational data is determined by a first model for controlling the autonomous vehicle; and
based on the one or more scores, updating the first model.