IP Library › Granted Patent US 12,523,494
Granted Patent B2
US 12,523,494 · App. 18/368,773 · Granted Jan 13, 2026

Systems and methods for generating heat maps

Inventor: Emily Lerner (Ypsilanti, MI)
Assignee: WOVEN BY TOYOTA, INC.
G01C21/3815G01C21/3841G01C21/387
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 12,523,494
App. No.
18/368,773
Granted
Jan 13, 2026
Kind
B2
Abstract

A system for generating a heat map corresponding to a difficult road topography includes one or more processors, network interface hardware, and one or more memory modules. When the machine-readable instructions are executed by the one or more processors, the system is caused to: receive vehicle swarm data related to at least the vehicle performance and the vehicle occupant, wherein at least a portion of the vehicle swarm data is generated by one or more vehicle sensors mounted to a vehicle of a vehicle swarm; determine the difficult road topography based on the vehicle swarm data; generate a heat map of the difficult road topography, wherein the heat map is based upon an aggregate number of times that a determination is made that a location has difficult road topography; and provide the heat map to a user though a user device.

Claims (45)

1 . A system for generating a heat map corresponding to a difficult road topography, the system comprising:

one or more processors;

network interface hardware communicatively coupled to the one or more processors;

one or more memory modules communicatively coupled to the one or more processors that store machine-readable instructions that when executed by the one or more processors, cause the system to:

receive vehicle swarm data related to at least a vehicle performance and a vehicle occupant, wherein at least a portion of the vehicle swarm data is generated by one or more vehicle sensors mounted to a vehicle of a vehicle swarm;

determine the difficult road topography based on the vehicle swarm data;

generate a heat map of the difficult road topography, wherein the heat map is based upon an aggregate number of times that a determination is made that a location has difficult road topography;

provide the heat map to a user though a user device;

receive historical data including at least historical road data and weather data; and

update the difficult road topography based on the historical data, wherein the heat map includes a toggle for the user to view the heat map in different weather.

2 . The system of claim 1 , wherein the difficult road topography includes at least one of a short ramp, a pot hole, a difficult junction, a sharp turn, a sharp curve, a flooded roadway, a blind spot, a sharp incline or decline, or an icy bridge.

3 . The system of claim 1 , wherein the difficult road topography is determined based on at least one of rates of decelerations, angles of steering, a likelihood to be in an accident, a driver's discomfort, or a driver's alertness.

4 . The system of claim 1 , wherein the heat map is comprised of a first color at a location of determined difficult road topography greater than or equal to a threshold and a second color at a location of determined difficult road topography less than the threshold.

5 . The system of claim 1 , wherein the vehicle sensors include at least one of a GPS, a predictive maintenance API, one or more cameras, one or more ultrasonic sensors, one or more audio sensors, or an echo location mapping.

6 . The system of claim 1 , wherein the machine-readable instructions further causes the system to:

determine a type of road topography based on the vehicle swarm data; and

organize the heat map based on the type of road topography, wherein the user can toggle the type of road topography.

7 . The system of claim 1 , wherein the heat map includes a toggle for the user to view the difficult road topography at different times of a day.

8 . The system of claim 1 , wherein

the vehicle is non-autonomous; and

the machine-readable instructions further cause the system to:

generate an alert to a driver of the vehicle when the vehicle approaches a location of determined difficult road topography above a threshold.

9 . The system of claim 1 , wherein

the vehicle is autonomous; and

the machine-readable instructions further cause the system to:

generate a path with a minimal amount of locations of determined difficult road topography above a threshold.

10 . A method for generating a heat map corresponding to a difficult road topography, the method comprising:

receiving vehicle swarm data related to at least a vehicle performance and a vehicle occupant, wherein at least a portion of the vehicle swarm data is generated by one or more vehicle sensors mounted to a vehicle of a vehicle swarm;

determining the difficult road topography based on the vehicle swarm data;

generating a heat map of the difficult road topography, wherein the heat map is based upon an aggregate number of times that a determination is made that a location has difficult road topography;

providing the heat map to a user though a user device;

receiving historical data including at least historical road data and weather data; and

updating the difficult road topography based on the historical data, wherein the heat map includes a toggle for the user to view the heat map in different weather.

11 . The method of claim 10 , wherein the difficult road topography includes at least one of a short ramp, a pot hole, a difficult junction, a sharp turn, a sharp curve, a flooded roadway, a blind spot, a sharp incline or decline, or an icy bridge.

12 . The method of claim 10 , wherein the difficult road topography is determined based on at least one of rates of decelerations, angles of steering, likelihood to be in an accident, a driver's discomfort, or the driver's alertness.

13 . The method of claim 10 , wherein the heat map is comprised of a first color at a location of determined difficult road topography greater than or equal to a threshold and a second color at a location of determined difficult road topography less than the threshold.

14 . The method of claim 10 , wherein the vehicle sensors include at least one of a GPS, a predictive maintenance API, one or more cameras, one or more ultrasonic sensors, one or more audio sensors, or an echo location mapping.

15 . The method of claim 10 , wherein the method further comprises:

determining a type of road topography based on the vehicle swarm data; and

organizing the heat map based on the type of road topography, wherein the user can toggle the type of road topography.

16 . The method of claim 10 , wherein the heat map includes a toggle for the user to view the difficult road topography at different times of a day.

17 . The method of claim 10 , wherein the method further comprises:

generating an alert to a driver of a non-autonomous vehicle when the vehicle approaches a location of determined difficult road topography above a threshold.

18 . The method of claim 10 , wherein the method further comprises:

generating a path for an autonomous vehicle with a minimal amount of locations of determined difficult road topography above a threshold.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 15, 2023
From: LERNER, EMILY
To: WOVEN BY TOYOTA, INC.
Reel/Frame 064921/0096 →
Continuity (1)
Related Publication 20250093176A1 · Mar 20, 2025
References Cited (16)
US 8694241B1 · Kadous et al. · 2014 [cited by applicant]
US 9251277B2 · Hampapur et al. · 2016 [cited by applicant]
US 9494440B2 · Sofinski et al. · 2016 [cited by applicant]
US 10163255B2 · Sorrento · 2018 [cited by applicant]
US 10533872B2 · Moore et al. · 2020 [cited by applicant]
US 20100332131A1 · Horvitz · 2010 [cited by examiner]
US 20120150436A1 · Rossano · 2012 [cited by examiner]
US 20140303806A1 · Bai · 2014 [cited by examiner]
US 20150347478A1 · Tripathi · 2015 [cited by examiner]
US 20180233042A1 · Zhang · 2018 [cited by examiner]
US 20190316309A1 · Wani · 2019 [cited by examiner]
US 20200406925A1 · Du · 2020 [cited by examiner]
US 20220044034A1 · RoyChowdhury · 2022 [cited by examiner]
US 20230067558A1 · Abe · 2023 [cited by examiner]
KR 102385866B1 · 2022 [cited by applicant]
Kshitij Dixit, “Making Navigation Easy—Using Waze for Navigation”, May 6, 2023, Zeo Route Planner (Year: 2023). [cited by examiner]