Affectiva Automotive AI for Driver Monitoring Systems
- 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.
- 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.
- 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.
Affectiva Automotive AI for Occupant Experience Monitoring
- Adapt environmental conditions, such as lighting and heating, based on levels of comfort and drowsiness. It could also help influence a change of the autonomous driving style if passengers are detected as being anxious or uncomfortable.
- Understand user frustration or confusion with virtual assistants and conversational interfaces, and design these to be emotion-aware, so they can respond in an appropriate and relevant manner. Imagine if the Human Machine Interface (HMI) in your car sensed it was frustrating you and used that feedback to improve the experience.
- Personalize content recommendations, such as video and music, based on the emotions of the passengers; make e-commerce purchase recommendations based on reactions to the content that is served or the route that is taken.
Deep Learning
Deep learning allows Affectiva to model more complex problems with higher accuracy than other machine learning techniques. It solves a variety of problems such as classification, segmentation and temporal modeling. It also allows for end-to-end learning of one or more complex tasks jointly including: face detection and tracking, speaker diarization, voice-activity detection, and emotion classification from face and voice.
Automotive Data Set
Affectiva has already analyzed over 7.5 million faces in 87 countries and has used this data to build its core Emotion AI technology. We also collect large amounts of spontaneous driver and occupant data so we can tune our existing algorithms for automotive environments, and develop new automotive metrics. We are building this large automotive data corpus through in-car data collection, lab simulation environments and data partnerships.
Real Time, On-Device
Our deep learning models provide accurate, real time estimates of emotions and cognitive states on mobile devices and embedded systems. Our focus is on building highly efficient models that achieve high accuracy with a minimal footprint. Our approach involves joint-training across a variety of tasks using shared layers between models combined with iterative benchmarking / profiling of on-device performance, and model compression - training compact models from larger models.
