IP Library Granted Patent US 12,606,213
Granted Patent B2
US 12,606,213 · App. 18/433,645 · Granted Apr 21, 2026

Providing compensation measures for preventing unexpected actions by a vehicle operator

Inventors: Seyhan Ucar (Mountain View, CA); Emrah Akin Sisbot (Mountain View, CA)
Assignees: Toyota Motor Engineering & Manufacturing North America, Inc.; Toyota Jidosha Kabushiki Kaisha
B60W60/0051B60W50/14B60W2555/20
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Quick Facts
Patent No.
US 12,606,213
App. No.
18/433,645
Granted
Apr 21, 2026
Kind
B2
Abstract

Systems and methods described herein relate to implementing compensation strategy planning. In one embodiment, a method includes determining a set of characteristics to be encountered by a vehicle when providing a pending notification; retrieving an action avoidance entry based on the pending notification and the set of characteristics; selecting a compensation measure from the action avoidance entry; and causing the vehicle to undertake the compensation measure prior to the vehicle providing the pending notification.

Claims (39)

1 . A system, comprising:

a processor; and

a memory communicably coupled to the processor and storing machine-readable instructions that, when executed by the processor, cause the processor to:

determine a set of characteristics to be encountered by a vehicle when providing a pending notification;

retrieve an action avoidance entry based on the pending notification and the set of characteristics;

select a compensation measure from the action avoidance entry; and

cause the vehicle to undertake the compensation measure prior to the vehicle providing the pending notification.

2 . The system of claim 1 , wherein the machine-readable instructions to determine a set of characteristics further includes determining a vehicle characteristic.

3 . The system of claim 1 , wherein the machine-readable instructions to determine a set of characteristics further includes determining a location.

4 . The system of claim 1 , wherein the machine-readable instructions to determine a set of characteristics further includes determining a weather condition.

5 . The system of claim 1 , wherein the machine-readable instructions that, when executed by the processor, further includes causing the processor to:

generate an action avoidance entry based on a set of recorded characteristics after a notification.

6 . The system of claim 5 , wherein the machine-readable instructions to generate the action avoidance entry based on the set of recorded characteristics after the notification further includes determining a second compensation measure.

7 . The system of claim 1 , wherein the machine-readable instructions that, when executed by the processor, further includes causing the processor to:

simulate an action avoidance entry.

8 . A non-transitory computer-readable medium including instructions that when executed by one or more processors cause the one or more processors to:

determine a set of characteristics to be encountered by a vehicle when providing a pending notification;

retrieve an action avoidance entry based on the pending notification and the set of characteristics;

select a compensation measure from the action avoidance entry; and

cause the vehicle to undertake the compensation measure prior to the vehicle providing the pending notification.

9 . The non-transitory computer-readable medium of claim 8 , wherein the instructions to determine a set of characteristics further includes determining a vehicle characteristic.

10 . The non-transitory computer-readable medium of claim 8 , wherein the instructions to determine a set of characteristics further includes determining a location.

11 . The non-transitory computer-readable medium of claim 8 , wherein the instructions to determine a set of characteristics further includes determining a weather condition.

12 . The non-transitory computer-readable medium of claim 8 , further comprising instructions that when executed by one or more processors cause the one or more processors to:

generate an action avoidance entry based on a set of recorded characteristics after a notification.

13 . The non-transitory computer-readable medium of claim 12 , wherein the instructions to generate the action avoidance entry based on the set of recorded characteristics after the notification further includes determining a second compensation measure.

14 . A method, comprising:

determining a set of characteristics to be encountered by a vehicle when providing a pending notification;

retrieving an action avoidance entry based on the pending notification and the set of characteristics;

selecting a compensation measure from the action avoidance entry; and

causing the vehicle to undertake the compensation measure prior to the vehicle providing the pending notification.

15 . The method of claim 14 , wherein the step of determining a set of characteristics includes determining a vehicle characteristic.

16 . The method of claim 14 , wherein the step of determining a set of characteristics includes determining a location.

17 . The method of claim 14 , wherein the step of determining a set of characteristics includes determining a weather condition.

18 . The method of claim 14 , further comprising:

generating an action avoidance entry based on a set of recorded characteristics after a notification.

19 . The method of claim 18 , wherein generating the action avoidance entry based on the set of recorded characteristics after the notification further includes determining a second compensation measure.

20 . The method of claim 14 , further comprising:

simulating an action avoidance entry.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 13, 2024
From: UCAR, SEYHAN; SISBOT, EMRAH AKIN
To: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC.; TOYOTA JIDOSHA KABUSHIKI KAISHA
Reel/Frame 066449/0365 →
Continuity (1)
Related Publication 20250249938A1 · Aug 7, 2025
References Cited (19)
US 9037455B1 · Faaborg · 2015 [cited by examiner]
US 11352013B1 · Srinivasan et al. · 2022 [cited by applicant]
US 11380198B2 · Ucar et al. · 2022 [cited by applicant]
US 20170113664A1 · Nix · 2017 [cited by applicant]
US 20170240185A1 · Li · 2017 [cited by examiner]
US 20200216079A1 · Mahajan · 2020 [cited by examiner]
US 20200342230A1 · Tsai et al. · 2020 [cited by applicant]
US 20200342235A1 · Tsai et al. · 2020 [cited by applicant]
US 20200342274A1 · ElHattab et al. · 2020 [cited by applicant]
US 20200342611A1 · ElHattab et al. · 2020 [cited by applicant]
US 20200344301A1 · ElHattab et al. · 2020 [cited by applicant]
US 20220068122A1 · Ucar et al. · 2022 [cited by applicant]
US 20230021643A1 · Neumann · 2023 [cited by examiner]
US 20240239379A1 · Kuehner · 2024 [cited by examiner]
US 20240294188A1 · Kume · 2024 [cited by examiner]
EP 4213124A1 · 2023 [cited by examiner]
WO WO2018094374A1 · 2018 [cited by examiner]
Madrid et al. “Matrix Profile XX: Finding and Visualizing Time Series Motifs of All Lengths using the Matrix Profile”, 2019 IEEE International Conference on Big Knowledge. 2019. [cited by applicant]
Mercer et al. “Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification”, 2021 IEEE International Conference on Data Mining. 2021. [cited by applicant]