Systems and methods for detecting windshield cracks

Patent metadata

Field	Value
Publication number	US-12198484-B2
Application number	US-202117405384-A
Country	US
Kind code	B2
Filing date	Aug 18, 2021
Priority date	Aug 18, 2021
Publication date	Jan 14, 2025
Grant date	Jan 14, 2025

Abstract

Systems and methods are disclosed for detecting a crack in an automotive windshield and alerting a user of the same. This can allow the user to repair the crack before the user might otherwise detect the crack by his/her own visual inspection. The windshield can be provided with emitters configured to emit signals (e.g., sound, light, etc.) and corresponding detectors configured to detect the emitted signals. Signal profiles or signatures can be stored that represent normal measurements when there is no crack. Upon detecting a signal signature that deviates from the stored normal signal signatures, the system can notify the user of a potential crack in the windshield. The system can also determine the location of the crack based upon which of the detectors detect a change in the detected signal.

First claim

What is claimed is:

1. A method of detecting a crack in a windshield and notifying a user regarding the crack, the method comprising:
emitting a first plurality of signals across at least a portion of the windshield from emitters located at a first region of the windshield;
detecting the first plurality of signals via detectors located at a second region of the windshield;
processing the first plurality of signals received by the detectors to derive first signal signatures;
storing the first signal signatures in memory;
emitting a second plurality of signals from the emitters;
detecting the second plurality of signals via the detectors;
processing the second plurality of signals received by the detectors to derive second signal signatures;
comparing the second signal signatures with the first signal signatures; and
notifying a user regarding a presence of a crack in the windshield in response to the second signal signatures deviating from the first signal signatures.

2. The method of claim 1 , wherein the emitters are optical emitters such that the steps of emitting include emitting light.

3. The method of claim 2 , further comprising orienting the optical emitters such that the light travels through total internal reflection (TIR) within the windshield.

4. The method of claim 1 , further comprising:
attaching the emitters along a first edge of the windshield; and
attaching the detectors along a second edge of the windshield opposite the first edge.

5. The method of claim 1 , wherein the emitters are acoustic emitters such that the steps of emitting include emitting sound.

6. The method of claim 5 , wherein the detectors are acoustic detectors, the method further comprising embedding the detectors in an array throughout the windshield.

7. The method of claim 1 , further comprising:
disposing the detectors in an array throughout the windshield;
determining a location of the crack based upon which of the detectors detect the second plurality of signals corresponding to the second signal signatures that deviate from the first plurality of signal signatures.

8. The method of claim 1 , further comprising:
storing a plurality of signal signatures from a plurality of vehicles into the memory; and
classifying the second signal signatures using a machine learning model using the plurality of signal signatures stored in the memory.

9. The method of claim 1 , further comprising:
coating a portion of the windshield with a transparent conducting oxide (TCO) with the detectors disposed along the TCO.

10. The system method of claim 9 , wherein the transparent conducting oxide includes at least one of the following:
M x ZnO 1+y where M=Al and/or Ga, x is between 0 and 1, and y is between −1 and 1;
M x TiO 2+y wherein M=Sb and/or W, x is between 0 and 1, and y is between −1 and 1; and
M x SnO 2+y wherein M=Sb, F, and/or In; x is between 0 and 1, and y is between −1 and 1.

Assignees

• Bosch Gmbh Robert

Inventors

• Kornbluth Mordechai
• Kim Soo
• Mailoa Jonathan
• Tuffile Charles
• Gadelrab Karim
• Samsonidze Georgy

Classifications

• G07C5/0833

  using audio means · CPC title

• G01N33/386

  Glass · CPC title

• G01N27/20

  Investigating the presence of flaws · CPC title

• G01N27/041

  of a solid body · CPC title

• G01N21/958 Primary

  Inspecting transparent materials {or objects, e.g. windscreens (for conveyed flat sheet or rod G01N21/896)} · CPC title