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Real-Time Analysis of Driver Behavior Using Machine Learning


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

Presented By:

Terry Walters
Terry Walters
Senior Solution Architect

Terry Walters is a Senior Solution Architect at Hazelcast®, a Java-based, open-source operational in-memory computing platform. He is well known as a speaker at user groups/conferences and he enjoys helping others succeed with web scale. Prior to Hazelcast, he worked 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.