In the pursuit of achieving Level 3 automated driving, the necessity for a driver’s constant availability to resume control remains crucial. Addressing this, an in-cabin smart system must effectively monitor and interpret the driver’s readiness. Current challenges include the accuracy of driver monitoring systems (DMS) in gauging attentiveness solely through eye gaze or steering wheel sensing. This may not be sufficient to assess the driver’s level of situational awareness. With a focus on multi-modal data fusion and deep learning models for simultaneous evaluation of in-cabin data and traffic scenes, these challenges can be tackled. By integrating in-cabin sensors and considering human factors, the model aims to revolutionize DMS enablers for a seamless transition between automated and manual driving. This presentation will also showcase research that emphasizes that assessing driver readiness requires a comprehensive approach beyond traditional methods, offering a promising solution to enhance the safety and efficiency of automated vehicle operation.

Discover more about:

• The critical need for accurate driver monitoring
• The challenges in current driver monitoring systems
• Multi-modal fusion and deep learning
• The role of human factor research in improving AI-based DMS

In this presentation, our team envisions and presents a concept Level 5 HMI using a VR headset, showcasing an HMI based on haptic knobs and an augmented reality windshield. With this premise, the control language adapts to the rider/occupant, and activity shifts from driving to work and relaxation. How might this control language now accommodate in-vehicle activity and affect in-cabin sensing?

Our team will present their latest iteration at this HMI and consider the implications of how certain design choices can affect in-cabin sensing use cases.

Further, by presenting this HMI concept, we are also interested in how might this conceptual future shift the priorities around in-cabin sensing. Namely:

• What activities will be most prevalent in Level 5 AV cabins?
• What societal trends will most significantly impact in-cabin sensing technologies?
• Which sensing technologies ultimately be best positioned to move between occupant rider activity?