Invention Grant
- Patent Title: Systems and methods for rear signal identification using machine learning
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Application No.: US15713491Application Date: 2017-09-22
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Publication No.: US10691962B2Publication Date: 2020-06-23
- Inventor: Xue Mei , Naoki Nagasaka , Kuan-Hui Lee , Danil V. Prokhorov
- Applicant: Toyota Motor Engineering & Manufacturing North America, Inc.
- Applicant Address: US TX Plano
- Assignee: Toyota Motor Engineering & Manufacturing North America, Inc.
- Current Assignee: Toyota Motor Engineering & Manufacturing North America, Inc.
- Current Assignee Address: US TX Plano
- Agency: Darrow Mustafa PC
- Agent Christopher G. Darrow
- Main IPC: G06K9/00
- IPC: G06K9/00 ; B60W30/09 ; G06K9/46 ; B60Q9/00 ; G06K9/66 ; G06K9/32 ; G06N3/08 ; G06N3/04 ; B60W30/095

Abstract:
System, methods, and other embodiments described herein relate to identifying rear indicators of a nearby vehicle. In one embodiment, a method includes, in response to detecting a nearby vehicle, capturing signal images of a rear portion of the nearby vehicle. The method includes computing a braking state for brake lights of the nearby vehicle that indicates whether the brake lights are presently active by analyzing the signal images according to a brake classifier. The method includes computing a turn state for rear turn signals of the nearby vehicle that indicates which of the turn signals are presently active by analyzing regions of interest from the signal images according to a turn classifier. The brake classifier and the turn classifier are comprised of a convolutional neural network and a long short-term memory recurrent neural network (LSTM-RNN). The method includes providing electronic outputs identifying the braking state and the turn state.
Public/Granted literature
- US20190092318A1 SYSTEMS AND METHODS FOR REAR SIGNAL IDENTIFICATION USING MACHINE LEARNING Public/Granted day:2019-03-28
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