Detection of hazardous driving using machine learning
Abstract
An autonomous driving system could create or exacerbate a hazardous driving situation due to incorrect machine learning, algorithm design, sensor limitations, environmental conditions or other factors. This technology presents solutions that use machine learning to detect when the autonomous driving system is in this state e.g., erratic or reckless driving and other behavior, in order to take remedial action to prevent a hazard such as a collision.
- Inventors:
- Issue Date:
- Research Org.:
- NVIDIA Corporation, Santa Clara, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1859974
- Patent Number(s):
- 11150663
- Application Number:
- 16/258,272
- Assignee:
- NVIDIA Corporation (Santa Clara, CA)
- Patent Classifications (CPCs):
-
G - PHYSICS G05 - CONTROLLING G05D - SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- DOE Contract Number:
- B609487
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 01/25/2019
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Shirvani, Philip, Bramley, Richard, Montrym, John, and Saxena, Nirmal. Detection of hazardous driving using machine learning. United States: N. p., 2021.
Web.
Shirvani, Philip, Bramley, Richard, Montrym, John, & Saxena, Nirmal. Detection of hazardous driving using machine learning. United States.
Shirvani, Philip, Bramley, Richard, Montrym, John, and Saxena, Nirmal. Tue .
"Detection of hazardous driving using machine learning". United States. https://www.osti.gov/servlets/purl/1859974.
@article{osti_1859974,
title = {Detection of hazardous driving using machine learning},
author = {Shirvani, Philip and Bramley, Richard and Montrym, John and Saxena, Nirmal},
abstractNote = {An autonomous driving system could create or exacerbate a hazardous driving situation due to incorrect machine learning, algorithm design, sensor limitations, environmental conditions or other factors. This technology presents solutions that use machine learning to detect when the autonomous driving system is in this state e.g., erratic or reckless driving and other behavior, in order to take remedial action to prevent a hazard such as a collision.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Oct 19 00:00:00 EDT 2021},
month = {Tue Oct 19 00:00:00 EDT 2021}
}
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