IP Library Granted Patent US 12,412,472
Granted Patent B2
US 12,412,472 · App. 18/740,468 · Granted Sep 9, 2025

Vehicle turn detection

Inventors: Varun Nagpal (Chicago, IL); Yasir Mukhtar (Des Plaines, IL); Jared S. Snyder (Chicago, IL); Connor Walsh (Lake Forest, IL)
Assignee: Arity International Limited
G08G1/056G06Q40/08G07C5/008G08G1/0112G08G1/0129G08G1/20
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,412,472
App. No.
18/740,468
Granted
Sep 9, 2025
Kind
B2
Abstract

A turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Claims (50)

1. A movement event detection apparatus comprising: a communication interface;

a processor; and

memory storing computer-readable instructions that, when executed by the processor, cause the movement event detection apparatus to:

receive, from a remote device, position information for a vehicle through the communication interface, the remote device being exterior to the vehicle;

determine headings of the vehicle based on the position information;

collect a plurality of turn events associated with the vehicle based on the determined headings;

filter the turn events down to those that are associated with a respective loss event, wherein the respective loss event occurred within a turning period that includes respective turn events or within a predefined amount of time of the respective turn events, and wherein the respective loss event is detected using sensor data collected from a respective vehicle;

select a set of one or more attributes to compare against the filtered turn events associated with the respective loss event;

determine a range of values of each respective attribute exhibiting at least a threshold percentage of loss events; and

calculate an overall risk associated with the vehicle based on the determined ranges of values.

2. The movement event detection apparatus of claim 1 , wherein the respective loss event is detected using sensor data collected from one or more vehicles within a specific proximity to the vehicle and within a time proximity to the respective loss event.

3. The movement event detection apparatus of claim 1 , wherein the set of attributes includes at least one of a pre-determined speed, a turn rate, a rate of acceleration, a rate of deceleration, or a direction of turn.

4. The movement event detection apparatus of claim 1 , wherein the movement event detection apparatus is further caused to:

determine a probability for a set of values of each attribute.

5. The movement event detection apparatus of claim 1 , wherein the movement event detection apparatus is further caused to:

determine whether the vehicle registered a turn based on the determined headings; and

employ vehicle functions upon determining that a turn event has occurred.

6. The movement event detection apparatus of claim 5 , wherein the vehicle functions include capping a speed of the vehicle with which the vehicle can make a turn.

7. The movement event detection apparatus of claim 5 , wherein the vehicle functions include modifying headlight directors based on a heading of the vehicle.

8. The movement event detection apparatus of claim 5 , wherein the vehicle functions include automatically activating a turn signal when the turn is detected.

9. A non-transitory computer-readable medium storing computer-readable instructions that, when executed by a processor, cause a movement event detection apparatus to:

receive, from a remote device, position information for a vehicle through a communication interface, the remote device being exterior to the vehicle;

determine headings of the vehicle based on the position information;

collect a plurality of turn events associated with the vehicle based on the determined headings;

filter the turn events down to those that are associated with a respective loss event, wherein the respective loss event occurred within a turning period that includes respective turn events or within a predefined amount of time of the respective turn events, and wherein the respective loss event is detected using sensor data collected from a respective vehicle;

select a set of one or more attributes to compare against the filtered set of turns associated with the respective loss event;

determine range of values of each respective attribute exhibiting at least a threshold percentage of loss events; and

calculate an overall risk associated with the vehicle based on the determined ranges of values.

10. The non-transitory computer-readable medium of claim 9 , wherein the respective loss event is detected using sensor data collected from one or more vehicles within a specific proximity to the vehicle and within a time proximity to the respective loss event.

11. The non-transitory computer-readable medium of claim 9 , wherein the set of attributes includes at least one of a pre-determined speed, a turn rate, a rate of acceleration, a rate of deceleration, or a direction of turn.

12. The non-transitory computer-readable medium of claim 9 , wherein the movement event detection apparatus is further caused to:

determine a probability for a set of values of each attribute.

13. The non-transitory computer-readable medium of claim 9 , wherein movement event detection apparatus is further caused to:

determine whether the vehicle registered a turn based on the determined headings; and

employ vehicle functions upon determining that a turn event has occurred.

14. The non-transitory computer-readable medium of claim 13 , wherein the vehicle functions include capping a speed of the vehicle with which the vehicle can make a turn.

15. The non-transitory computer-readable medium of claim 13 , wherein the vehicle functions include modifying headlight directors based on a heading of the vehicle.

16. The non-transitory computer-readable medium of claim 13 , wherein the vehicle functions include automatically activating a turn signal when the turn is detected.

17. A computer-implemented method comprising:

receiving, from a remote device, position information for a vehicle through a communication interface, the remote device being exterior to the vehicle;

determining headings of the vehicle based on the position information;

collecting a plurality of turn events associated with the vehicle based on the determined headings;

filtering the turn events down to those that are associated with a respective loss event, wherein the respective loss event occurred within a turning period that includes respective turn events or within a predefined amount of time of the respective turn events, and wherein the respective loss event is detected using sensor data collected from a respective vehicle;

selecting a set of one or more attributes to compare against the filtered set of turns associated with the respective loss event;

determining range of values of each respective attribute exhibiting at least a threshold percentage of loss events; and

calculating an overall risk associated with the vehicle based on the determined ranges of values.

18. The computer-implemented method of claim 17 , wherein the respective loss event is detected using sensor data collected from one or more vehicles within a specific proximity to the vehicle and within a time proximity to the respective loss event.

19. The computer-implemented method of claim 17 , wherein the set of attributes includes at least one of a pre-determined speed, a turn rate, a rate of acceleration, a rate of deceleration, or a direction of turn.

20. The computer-implemented method of claim 17 , further comprising:

determining a probability for a set of values of each attribute.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 23, 2025
From: NAGPAL, VARUN; MUKHTAR, YASIR; SNYDER, JARED S.; WALSH, CONNOR
To: ALLSTATE INSURANCE COMPANY
Reel/Frame 071810/0913 →
SUPPLEMENTAL MEMO TO PURCHASE AGREEMENT Recorded Jul 23, 2025
From: ALLSTATE INSURANCE COMPANY
To: ARITY INTERNATIONAL LIMITED
Reel/Frame 072197/0297 →
Continuity (7)
Continuation 18121354 · Mar 14, 2023
Continuation 17007550 · Aug 31, 2020
Continuation 16166798 · Oct 22, 2018
Continuation 15873211 · Jan 17, 2018
Continuation 15496336 · Apr 25, 2017
Continuation 15251556 · Aug 30, 2016
Related Publication 20240331534A1 · Oct 3, 2024
References Cited (40)
US 5390122A · Michaels · 1995 [cited by applicant]
US 5462106A · Hanna · 1995 [cited by applicant]
US 5469158A · Morita · 1995 [cited by examiner]
US 6014610A · Judge · 2000 [cited by applicant]
US 6028537A · Suman · 2000 [cited by applicant]
US 6178368B1 · Olake · 2001 [cited by applicant]
US 6502033B1 · Phuyal · 2002 [cited by examiner]
US 8548739B2 · Lee · 2013 [cited by applicant]
US 8694224B2 · Chundrlik, Jr. · 2014 [cited by applicant]
US 8781669B1 · Teller · 2014 [cited by applicant]
US 8930231B2 · Bowne · 2015 [cited by applicant]
US 9127946B1 · Menon et al. · 2015 [cited by applicant]
US 9147353B1 · Slusar · 2015 [cited by applicant]
US 9176500B1 · Teller · 2015 [cited by applicant]
US 9666067B1 · Nagpal · 2017 [cited by applicant]
US 9905127B1 · Nagpal · 2018 [cited by applicant]
US 10140857B2 · Nagpal · 2018 [cited by applicant]
US 20040133346A1 · Bye · 2004 [cited by applicant]
US 20040236477A1 · Chowdhary · 2004 [cited by applicant]
US 20080004806A1 · Kimura · 2008 [cited by examiner]
US 20090037052A1 · Ogasawara · 2009 [cited by examiner]
US 20110066304A1 · Taylor · 2011 [cited by applicant]
US 20110117903A1 · Bradley · 2011 [cited by applicant]
US 20110125402A1 · Mitsugi · 2011 [cited by applicant]
US 20140142786A1 · Huang · 2014 [cited by examiner]
US 20140257863A1 · Maastricht · 2014 [cited by applicant]
US 20150006099A1 · Pham · 2015 [cited by applicant]
US 20150112730A1 · Binion · 2015 [cited by examiner]
US 20150153189A1 · Kim · 2015 [cited by examiner]
US 20160086285A1 · Jordan Peters · 2016 [cited by examiner]
US 20160171521A1 · Ramirez · 2016 [cited by examiner]
US 20160189323A1 · Wakabayashi · 2016 [cited by examiner]
CN 104504531 · 2015 [cited by applicant]
WO 2004077283 · 2004 [cited by applicant]
WO 2013107978 · 2013 [cited by applicant]
First Examination Report, IN Appln No. 201927011130, Dec. 2, 2020. [cited by applicant]
Self-Driving Cars and Insurance, Insurance Information Institute, Jul. 2016, downloaded from <http://www.iii.org/issue-update/self-driving-cars-and-insurance> on Aug. 30, 2026. [cited by applicant]
International Search Report, PCT/US2017/049120, Sep. 29, 2017. [cited by applicant]
Extended European Search Report, EP 17847368, Mar. 18, 2020. [cited by applicant]
Office Action, CA 3036193, Apr. 28, 2020. [cited by applicant]