US 12,479,477 B2
Courtesy lane selection paradigm
Adam Shoemaker, Blacksburg, VA (US); John Blankenhorn, Blacksburg, VA (US); Garrett Madsen, Blacksburg, VA (US); Joshua Petrin, Blacksburg, VA (US); Ajay Tulsyan, Blacksburg, VA (US); Paul Brown, Blacksburg, VA (US); Savio Pereira, Blacksburg, VA (US); William Davis, Blacksburg, VA (US); Daniel Fernández, Blacksburg, VA (US); and Yexuan Hao, Blacksburg, VA (US)
Assigned to Torc Robotics, Inc., Blacksburg, VA (US)
Filed by TORC Robotics, Inc., Blacksburg, VA (US)
Filed on Aug. 18, 2023, as Appl. No. 18/235,793.
Prior Publication US 2025/0058803 A1, Feb. 20, 2025
This patent is subject to a terminal disclaimer.
Int. Cl. B60W 60/00 (2020.01); G08G 1/16 (2006.01)
CPC B60W 60/00276 (2020.02) [G08G 1/167 (2013.01); B60W 2552/05 (2020.02); B60W 2552/10 (2020.02); B60W 2554/4041 (2020.02); B60W 2554/4045 (2020.02); B60W 2554/4049 (2020.02); B60W 2554/802 (2020.02); B60W 2556/40 (2020.02)] 14 Claims
OG exemplary drawing
 
1. A method for navigation planning for an autonomous vehicle, the method comprising:
obtaining, by a processor of an autonomous vehicle, sensor data from a plurality of sensors onboard the autonomous vehicle for a roadway, the roadway including a current travel lane of the autonomous vehicle, an adjacent travel lane that is adjacent to the current travel lane, and a tapering travel lane that is adjacent to the current lane of travel and on an opposite side of the current travel lane from the adjacent travel lane;
identifying, by the processor, a merging vehicle in the tapering lane by applying an object recognition engine on the sensor data;
identifying a first vehicle and a second vehicle in the adjacent travel lane by applying the object recognition engine on the sensor data;
determining a traffic gap in the adjacent travel lane, the traffic gap having an amount of distance between the first vehicle and the second vehicle in the adjacent travel lane;
obtaining, by the processor, a first cost value for the current travel lane and a second cost value for the adjacent travel lane based upon the sensor data, each cost value representing a cost for traveling in the corresponding lane;
determining, by the processor, that the first cost value is comparatively lower than the second cost value when the processor fails to identify the traffic gap satisfying a gap distance;
updating, by the processor, a control command for causing the autonomous vehicle to continue driving in the current travel lane; and
transmitting, by the processor, the control command that causes the autonomous vehicle to continue driving in the current travel lane to an operating module for driving the autonomous vehicle.