IP Library › Granted Patent US 12,726,542
Granted Patent B2
US 12,726,542 · App. 18/732,068 · Granted Sep 1, 2026

Low bandwidth protocol for streaming sensor data

Inventors: Caleb William Locke Foust (San Francisco, CA); Hourann William Bosci (San Francisco, CA); Konstantine Mushegian (San Francisco, CA)
Assignee: Embark Trucks Inc.
H04L67/12B60W50/0205B60W50/04B60W50/06H04L47/34H04L47/365H04L47/43B60W2556/45
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Quick Facts
Patent No.
US 12,726,542
App. No.
18/732,068
Granted
Sep 1, 2026
Kind
B2
Abstract

Provided are systems, methods and computer program code for transmitting vehicle data to remote monitoring systems using a low bandwidth protocol.

Claims (51)

1 . A method performed by a user device for remote monitoring of an autonomous vehicle, comprising:

transmitting to a remote system an identification of the autonomous vehicle to be monitored;

in response to providing the identification to the remote system, receiving from the remote system a plurality of data frames associated with the autonomous vehicle, the plurality of data frames having one or more of a plurality of data types, the plurality of data types comprising vehicle signal data, vehicle diagnostic data, and object data, each frame in the plurality of data frames comprising a timestamp and a sequence ID;

assembling the received plurality of data frames based on data type, sequence ID, and timestamp to reconstruct an ordered sequence of telemetry data for the autonomous vehicle;

making a determination that a particular data type is one that requires a particular context map, the particular context map associating each of a plurality of message identifiers with a corresponding plurality of message labels, wherein data frames of the particular data type each further comprise at least one message identifier;

based on the determination, generating using the particular context map a message label for each data frame of the particular data type based on the corresponding message identifier; and

displaying the plurality of data frames and generated message labels on a user interface of the user device.

2 . The method of claim 1 , wherein data frames of the particular data type each further comprise a context identifier, the method further comprising selecting the particular context map from a plurality of context maps based on the context identifier.

3 . The method of claim 1 , further comprising transmitting to the remote system the particular data type, wherein the plurality of data frames are of the particular data type.

4 . The method of claim 1 , wherein the identification of the autonomous vehicle is provided by a remote user.

5 . The method of claim 1 , wherein the generated message label for each data frame is of a greater size than the corresponding message identifier.

6 . The method of claim 1 , wherein the particular context map is a current context map.

7 . The method of claim 1 , wherein the particular data type is vehicle diagnostic data.

8 . The method of claim 7 , wherein the particular context map is a mapping of vehicle diagnostic data status names to integers.

9 . The method of claim 1 , further comprising:

displaying the plurality of data frames and generated message labels in a first display area within the user interface;

receiving streaming data from the autonomous vehicle; and

displaying the streaming data in the user interface in a second display area within the user interface, separate from the first display area.

10 . The method of claim 9 , wherein displaying the streaming data is time-synchronized with displaying the plurality of data frames.

11 . A non-transitory computer-readable medium storing a program for remote monitoring of an autonomous vehicle, which when executed by a computer, configures the computer to:

transmit to a remote system an identification of the autonomous vehicle to be monitored;

in response to providing the identification to the remote system, receive from the remote system a plurality of data frames associated with the autonomous vehicle, the plurality of data frames having one or more of a plurality of data types, the plurality of data types comprising vehicle signal data, vehicle diagnostic data, and object data, each frame in the plurality of data frames comprising a timestamp and a sequence ID;

assemble the received plurality of data frames based on data type, sequence ID, and timestamp to reconstruct an ordered sequence of telemetry data for the autonomous vehicle;

make a determination that a particular data type is one that requires a particular context map, the particular context map associating each of a plurality of message identifiers with a corresponding plurality of message labels, wherein data frames of the particular data type each further comprise at least one message identifier;

based on the determination, generate using the particular context map a message label for each data frame of the particular data type based on the corresponding message identifier; and

display the plurality of data frames and generated message labels on a user interface.

12 . The non-transitory computer-readable medium of claim 11 , wherein data frames of the particular data type each further comprise a context identifier, and wherein the program, when executed by the computer, further configures the computer to select the particular context map from a plurality of context maps based on the context identifier.

13 . The non-transitory computer-readable medium of claim 11 , wherein the generated message label for each data frame is of a greater size than the corresponding message identifier.

14 . The non-transitory computer-readable medium of claim 11 , wherein the particular data type is vehicle diagnostic data.

15 . The non-transitory computer-readable medium of claim 14 , wherein the particular context map is a mapping of vehicle diagnostic data status names to integers.

16 . The non-transitory computer-readable medium of claim 11 , wherein the program, when executed by the computer, further configures the computer to:

display the plurality of data frames and generated message labels in a first display area within the user interface;

receive streaming data from the autonomous vehicle; and

display the streaming data in the user interface in a second display area within the user interface, separate from the first display area,

wherein the display of the streaming data is time-synchronized with the display of the plurality of data frames.

17 . A system for remote monitoring of an autonomous vehicle, comprising:

a memory configured to store data frames received from a remote system; and

a processor configured to:

transmit to the remote system an identification of the autonomous vehicle to be monitored;

in response to providing the identification, receive from the remote system a plurality of data frames associated with the autonomous vehicle, the plurality of data frames having one or more of a plurality of data types, the plurality of data types comprising vehicle signal data, vehicle diagnostic data, and object data, each frame in the plurality of data frames comprising a timestamp and a sequence ID;

assemble the received plurality of data frames based on data type, sequence ID, and timestamp to reconstruct an ordered sequence of telemetry data for the autonomous vehicle;

make a determination that a particular data type is one that requires a particular context map, the particular context map associating each of a plurality of message identifiers with a corresponding plurality of message labels, wherein data frames of the particular data type each further comprise at least one message identifier;

based on the determination, generate using the particular context map a message label for each data frame of the particular data type based on the corresponding message identifier; and

display the plurality of data frames and generated message labels on a user interface,

wherein the generated message label for each data frame is of a greater size than the corresponding message identifier.

18 . The system of claim 17 , wherein data frames of the particular data type each further comprise a context identifier, and wherein the processor is further configured to select the particular context map from a plurality of context maps based on the context identifier.

19 . The system of claim 17 , wherein the particular data type is vehicle diagnostic data, and the particular context map is a mapping of vehicle diagnostic data status names to integers.

20 . The system of claim 17 , wherein the processor is further configured to:

display the plurality of data frames and generated message labels in a first display area within the user interface;

receive streaming data from the autonomous vehicle; and

display the streaming data in the user interface in a second display area within the user interface, separate from the first display area.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 4, 2024
From: FOUST, CALEB WILLIAM LOCKE; BOSCI, HOURANN WILLIAM; MUSHEGIAN, KONSTANTINE
To: EMBARK TRUCKS INC.
Reel/Frame 067613/0893 →
Continuity (3)
Continuation 18094633 · Jan 9, 2023
Continuation 17842150 · Jun 16, 2022
Related Publication 20250023947A1 · Jan 16, 2025
References Cited (47)
US 10560281B2 · Hellenthal · 2020 [cited by applicant]
US 10880409B2 · Maluf · 2020 [cited by examiner]
US 11402220B2 · Rabel et al. · 2022 [cited by applicant]
US 11553043B1 · Foust · 2023 [cited by examiner]
US 20130070745A1 · Nixon · 2013 [cited by examiner]
US 20160112216A1 · Sargent · 2016 [cited by applicant]
US 20180050704A1 · Tascione · 2018 [cited by applicant]
US 20190041835A1 · Cella · 2019 [cited by applicant]
US 20190095711A1 · Northcutt et al. · 2019 [cited by applicant]
US 20190132709A1 · Graefe · 2019 [cited by applicant]
US 20190279440A1 · Ricci · 2019 [cited by applicant]
US 20190279447A1 · Ricci · 2019 [cited by applicant]
US 20190344663A1 · Ricci · 2019 [cited by applicant]
US 20200209855A1 · Shen · 2020 [cited by examiner]
US 20200209861A1 · Priyadarshi · 2020 [cited by examiner]
US 20200225655A1 · Cella · 2020 [cited by examiner]
US 20200273268A1 · Bhattacharyya · 2020 [cited by applicant]
US 20200314940A1 · Park · 2020 [cited by applicant]
US 20200394409A1 · Cristache · 2020 [cited by examiner]
US 20210034042A1 · Lee · 2021 [cited by applicant]
US 20210037120A1 · Lee · 2021 [cited by applicant]
US 20210056058A1 · Lee et al. · 2021 [cited by applicant]
US 20210160315A1 · Linn-Moran et al. · 2021 [cited by applicant]
US 20210202067A1 · Williams · 2021 [cited by applicant]
US 20210312725A1 · Milton · 2021 [cited by applicant]
US 20210342020A1 · Jorasch · 2021 [cited by applicant]
US 20220066456A1 · Ebrahimi Afrouzi · 2022 [cited by examiner]
US 20220116736A1 · Williams · 2022 [cited by applicant]
US 20220126864A1 · Moustafa · 2022 [cited by examiner]
US 20220141708A1 · Arrobo Vidal et al. · 2022 [cited by applicant]
US 20220188867A1 · Farmer · 2022 [cited by examiner]
US 20220201757A1 · Cruz et al. · 2022 [cited by applicant]
US 20220248296A1 · Merwaday et al. · 2022 [cited by applicant]
US 20220374515A1 · Bridges · 2022 [cited by applicant]
US 20230110467A1 · Jha · 2023 [cited by examiner]
US 20230353422A1 · Neumann · 2023 [cited by examiner]
US 20250097047A1 · Cai · 2025 [cited by examiner]
US 20250335408A1 · Chowdhary · 2025 [cited by examiner]
AU 2014342474A1 · 2016 [cited by applicant]
AU 2018202791A1 · 2018 [cited by examiner]
DE 102021110736A1 · 2021 [cited by applicant]
ES 2445718T3 · 2014 [cited by examiner]
WO 2019134758A1 · 2019 [cited by applicant]
WO WO2020139377A1 · 2020 [cited by examiner]
WO WO2020139395A1 · 2020 [cited by examiner]
WO WO2021194590A1 · 2021 [cited by examiner]
WO 2022075769A1 · 2022 [cited by applicant]