Every day, there are more than 1,000 injuries and nine fatalities caused by distracted driving in the U.S. alone.

With the deployment of autonomous driving capabilities, OEMs and Tier 1s face even more safety concerns and considerations.

To improve road safety, OEMs and Tier 1s must have a deep understanding of driver emotions, cognitive states, and reactions to the vehicle systems and driving experience. Using cameras and microphones, Affectiva Automotive AI unobtrusively measures, in real time, complex and nuanced emotional and cognitive states from face and voice. This next generation in-cabin software uses deep learning and real world driver data. It enables OEMs and Tier 1s to measure driver impairment.

Driver State Monitoring

Driver monitoring systems on the market today are still in their infancy, and there’s a significant gap when it comes to effectively recognizing complex and nuanced cognitive and emotional states.

Using AI and deep learning, Affectiva Automotive AI takes driver state monitoring to the next level, analyzing both face and voice for levels of driver impairment caused by physical distraction, mental distraction from cognitive load or anger, drowsiness and more. With this “people data”, the car infotainment or ADAS can be designed to take appropriate action. In semi-autonomous vehicles, awareness of driver state also builds trust between people and machine, enabling an eyes-off-road experience and helping solve the “handoff” challenge.

Affectiva Automotive AI provides data to inform potential vehicle actions:

  • Monitor levels of driver fatigue and distraction to enable appropriate alerts and interventions that correct dangerous driving. An audio or display alert instructs the driver to remain engaged; the seat belt vibrates to jolt the driver to attention.
  • Monitor driver anger to enable interventions or route alternatives that avoid road rage. A virtual assistant guides the driver to take a deep breath, the driver’s preferred soothing playlist comes on, the GPS suggests a stop along the way.

Address handoff challenge between driver and car in semi-autonomous vehicles, when sensing driver fatigue, anger or distraction, the autonomous AI can determine if the car must take over control. And when the driver is alert and engaged, the vehicle can pass back control.