This presentation delves into the realm of car sickness within the context of autonomous vehicles (AVs). With the increasing adoption of self-driving cars, understanding and addressing passenger discomfort due to motion sickness is paramount. Leveraging in-car cameras, infrared, and thermography technologies, TNO’s team explores innovative methods to monitor occupants’ physiological responses and driver states. Through the fusion of conventional and infrared imagery, coupled with deep learning techniques, real-time assessment of passengers’ discomfort levels can be achieved. Several technological and methodological challenges that currently still prevent application in real car driving will be discussed. If solved, that would also open up possibilities to extend this kind of in-cabin sensing to, e.g., the generic concept of driver state monitoring.

Key points of discussion include:

• Utilization of in-car cameras to monitor passenger well-being in AVs
• Integration of infrared and thermography imaging for enhanced data collection
• Implementation of deep learning algorithms for real-time analysis of occupants’ physiological cues
• Strategies to utilize technologies to mitigate car sickness effectively