US 12,235,121 B2
Identifying objects for display in a situational-awareness view of an autonomous-vehicle environment
Robert Earl Rasmusson, San Francisco, CA (US); Taggart Matthiesen, Kentfield, CA (US); Craig Dehner, San Francisco, CA (US); Linda Dong, San Francisco, CA (US); Frank Taehyun Yoo, San Carlos, CA (US); Karina van Schaardenburg, San Francisco, CA (US); John Tighe, San Francisco, CA (US); Matt Vitelli, San Francisco, CA (US); Jisi Guo, San Francisco, CA (US); and Eli Guerron, San Francisco, CA (US)
Assigned to Lyft, Inc., San Francisco, CA (US)
Filed by Lyft, Inc., San Francisco, CA (US)
Filed on Sep. 26, 2023, as Appl. No. 18/474,507.
Application 18/474,507 is a continuation of application No. 17/390,672, filed on Jul. 30, 2021, granted, now 11,788,856.
Application 17/390,672 is a continuation of application No. 15/812,645, filed on Nov. 14, 2017, granted, now 11,080,534, issued on Aug. 3, 2021.
Claims priority of provisional application 62/422,025, filed on Nov. 14, 2016.
Prior Publication US 2024/0125612 A1, Apr. 18, 2024
Int. Cl. B60W 60/00 (2020.01); B62D 15/02 (2006.01); G01C 21/36 (2006.01); G05D 1/00 (2006.01); G06F 18/24 (2023.01); G06T 7/20 (2017.01); G06V 20/56 (2022.01)
CPC G01C 21/3638 (2013.01) [B60W 60/00253 (2020.02); B62D 15/0285 (2013.01); G01C 21/365 (2013.01); G05D 1/0044 (2013.01); G05D 1/0088 (2013.01); G05D 1/0212 (2013.01); G05D 1/0246 (2013.01); G05D 1/0274 (2013.01); G06F 18/24 (2023.01); G06T 7/20 (2013.01); G06V 20/56 (2022.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); B60W 2554/00 (2020.02); B60W 2555/20 (2020.02); B60W 2556/10 (2020.02); G06T 2207/30252 (2013.01); G06T 2207/30256 (2013.01); G06T 2207/30261 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising, by one or more computing devices:
receiving sensor data associated with an object external to a vehicle, the sensor data comprising a sequence of data packets, wherein each data packet in the sequence of data packets corresponds to a time frame in a sequence of time frames;
determining a classification of the object based at least on the sensor data;
determining that a particular data packet corresponding to a particular time frame in the sequence of time frames is corrupt or missing;
for the particular data packet that is corrupt or missing, generating a replacement data packet based on one or more data packets that correspond to one or more time frames adjacent to the particular time frame in the sequence of time frames;
generating a sequence of visual representations of the object corresponding to the sequence of time frames, wherein at least one visual representation in the sequence of visual representations corresponding to the particular time frame is generated based on the replacement data packet; and
sending instructions to display the sequence of visual representations of the object, wherein the display of the sequence of visual representations lags the sequence of time frames by a defined time latency.