Understand complex and nuanced states of driver impairment. Provide "quantified driver" services that provide insight into what driving modes, vehicles settings, routes, and times get you to your destination in the best emotional state.
Insight into the state of the occupants helps improve their comfort by adapting music, lighting, temperature. Understanding passenger engagement with content allows for personalized recommendations and for opportunities to monetize media data.
Insight into the state of the cabin helps personalize safety features (seatbelt, airbag), content (video, music and advertising), and the environment of the cabin (heating and lighting). It can also detect if a child or objects have been left behind.
Affectiva's deep learning algorithms are trained and tested with our large, proprietary data set that is representative of human appearance variances and real-world use cases. Global in nature, our data is diverse in age, gender and ethnicity, helping us mitigate for bias.
As the industry moves to holistic in-cabin sensing, interior cameras will be placed where they have a view of the entire cabin, including the driver and passengers in it. Overhead camera positions are proving ideal, and Affectiva has tuned its models for this location.
Affectiva's deep learning models are tuned to run on automotive Systems on Chip (SoC). We have optimized our footprint to run alongside other services, in real time, with automotive grade accuracy. Our models support TensorFlow Lite deep learning runtime.
With In-Cabin Sensing, ridesharing drivers and brands can deliver five-star rides that keep their customers feeling safe, happy, comfortable and entertained.
Distractions are a major risk for teen drivers. Parents can’t always be along for the ride, but In-Cabin Sensing can reinforce good driving habits for them and keep teens safe.
In-Cabin Sensing can make family trips safer and more relaxing by powering the systems that control driver and passenger safety and cabin conditions.