Detecting Distracted Drivers Using Convolution Neural Networks (Deep Learning)
Deep Learning In-Class Bootcamp
Fully Immersive and Evening Bootcamps
Online Live Instructor-Led Sessions
116 West 23rd Street 5th Floor New York City, NY 10011
Washington, D.C (Dupont Circle)
1875 Connecticut Ave NW 10th Floor Washington DC 20009
San Fransisco, CA (Civic Center)
1161 Mission St San Francisco CA 94103
San Fransisco, CA
1775 Tysons Blvd Tysons VA 22102
Objective of the course
Distracted drivers are quietly causing a staggering amount of serious car accidents. In fact, driver distractions are the leading cause of most auto accidents. As experienced car accident lawyers in San Diego, we have handled plenty of automobile accidents that were caused by driver distractions or driver inattentiveness.
Here are some common driver distractions:
- Talking on a cell phone
- Sending text messages
- Reaching for a moving object inside the vehicle
- Looking at an object or event outside of the vehicle
- Reading a book
- Eating food
- Applying makeup.
According to a study released by the National Highway Traffic Safety Administration (NHTSA) and the Virginia Tech Transportation Institute (VTTI), 80 percent of automobile accidents and 65 percent of near-accidents involve at least some form of driver distraction within three seconds of the crash or near-miss. As a result, police and traffic wardens everywhere have begun aggressively ticketing people that engage in distracted driving and endanger other drivers, passengers and pedestrians.
Fortunately we can apply deep learning to automatically detect distracted driver using convolutional neural network (CNN, or ConvNet)
This course is designed to teach you how to build deep-learning models to detect what a driver is doing in a car given the driver’s images.
- Enroll Now
- Enroll Now