US 12,229,982 B2
Offline optimization of sensor data for agent trajectories
Filippo Brizzi, London (GB); Luca del Pero, London (GB); Tayyab Naseer, London (GB); and Lorenzo Peppoloni, London (GB)
Assigned to Lyft, Inc., San Francisco, CA (US)
Filed by Lyft, Inc., San Francisco, CA (US)
Filed on Mar. 2, 2021, as Appl. No. 17/190,312.
Prior Publication US 2022/0284619 A1, Sep. 8, 2022
Int. Cl. G06T 7/70 (2017.01); G06F 18/22 (2023.01); H04N 13/204 (2018.01); B60W 10/18 (2012.01); B60W 10/20 (2006.01); B60W 30/00 (2006.01); B60W 60/00 (2020.01)
CPC G06T 7/70 (2017.01) [G06F 18/22 (2023.01); H04N 13/204 (2018.05); B60W 10/18 (2013.01); B60W 10/20 (2013.01); B60W 30/00 (2013.01); B60W 60/001 (2020.02); B60W 2420/403 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/30241 (2013.01); G06T 2207/30252 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, at a computing system, sensor data captured by a camera-based sensor system associated with a vehicle in an environment;
processing, by the computing system, the received sensor data and thereby determining a sensor-based track for an agent that is identified within the sensor data, wherein the sensor-based track comprises a time-sequence of sensor-based positions of the agent;
generating, by the computing system, a first updated time-sequence of sensor-based positions of the agent by performing a first optimization operation on the time-sequence of sensor-based positions, wherein the first optimization operation includes processing at least two or more sensor-based positions of the time-sequence of sensor-based positions by beginning with a sensor-based position having a highest confidence in the time-sequence of sensor-based position observations and proceeding in a first direction;
after generating the first updated time-sequence of sensor-based positions, generating, by the computing system, a second updated time-sequence of sensor-based positions of the agent by performing a second optimization operation on the first updated time-sequence of sensor-based positions, wherein the second optimization operation includes processing at least two or more sensor-based positions of the first updated time-sequence of sensor-based positions in a second direction opposite the first direction;
deriving, by the computing system, a trajectory for the agent in the environment based on the second updated time-sequence of sensor-based positions of the agent; and
storing the derived trajectory, wherein the derived trajectory is thereafter utilized to perform a task that benefits from increased-accuracy trajectories.