US 12,223,410 B2
Lane selection using machine learning
Thomas Deselaers, Zurich (CH); and Victor Carbune, Zurich (CH)
Assigned to GOOGLE LLC, Mountain View, CA (US)
Filed by GOOGLE LLC, Mountain View, CA (US)
Filed on Feb. 27, 2024, as Appl. No. 18/589,391.
Application 18/589,391 is a continuation of application No. 17/138,866, filed on Dec. 30, 2020, granted, now 11,915,115, issued on Feb. 27, 2024.
Claims priority of provisional application 62/956,231, filed on Dec. 31, 2019.
Prior Publication US 2024/0202490 A1, Jun. 20, 2024
Int. Cl. G06N 3/006 (2023.01); B60W 30/18 (2012.01); G06F 18/21 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01)
CPC G06N 3/006 (2013.01) [B60W 30/18163 (2013.01); G06F 18/217 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/588 (2022.01); B60W 2420/403 (2013.01); B60W 2520/10 (2013.01); B60W 2552/10 (2020.02); B60W 2555/20 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A method for selecting a lane in a multi-lane road segment for a vehicle travelling on the road segment, the method comprising:
identifying, in a plurality of lanes and in a region ahead of the vehicle, another vehicle defining a target;
applying an optical flow technique to track the target during a period of time, to generate an estimate of how fast traffic moves; and
applying the estimate to machine learning (ML) model to generate a recommendation which one of the plurality of lanes the vehicle is to choose.