Real-Time Analysis of Driver Behavior Using Machine Learning
|Date||Available On Demand|
Understanding driver behavior via the use of connected cars can help organizations make data-driven decisions to reduce safety risks, improve commercial driver productivity, and streamline fleet operations.
One area of analysis pertains to distracted driving, which poses a huge risk to the driver and the environment, in addition to financial loss. But distracted driving is not always manifested in obvious patterns, so the use of machine learning can help to uncover the indicators that drivers are distracted.
In this webinar by Hazelcast and Intel, we will show a method for the non-intrusive and real-time detection of visual distraction. We will discuss:
- Applying machine learning to multiple drivers’ historical data to determine driving patterns
- Using these patterns in real-time to identify the level of driver distraction
- How such insight applies to other environments pertaining to driver safety, driver riskiness for insurance, and delivery efficiency
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Terry Walters, Senior Solution Architect at Hazelcast
Terry Walters is a Senior Solution Architect at Hazelcast, a Java-based, open-source operational in-memory computing platform. He is a speaker at local user groups. He enjoys helping others succeed with web-scale. His employment includes many industry leaders such as AT&T, Verizon, McKesson, UPS, and the formally known as BEA Systems. Walters holds a bachelor’s degree in computer science.