• How does interior sensing contribute to the overall safety of software-defined and autonomous vehicles?
• What are the biggest safety concerns for passengers in a self-driving vehicle, and how can in-cabin sensors help address them?
• How can interior sensing systems detect and respond to emergencies, such as passenger medical issues or unsafe behavior?
• In a vehicle without a human driver, how can interior monitoring help ensure passengers follow safety protocols (e.g., seatbelt use, proper seating positions)?
• What are the biggest challenges in making passengers feel comfortable with being monitored, while ensuring safety remains a top priority?

Interior sensing is a critical aspect of self-driving vehicles, ensuring passenger safety, comfort, and regulatory compliance. However, several technical challenges must be addressed to enable robust and reliable occupant monitoring. This session explores the limitations of current sensor technologies (e.g., cameras, LiDAR, radar), the role of AI and machine learning in improving detection accuracy, and the complexities of fusing data from multiple sensors. Additionally, we will discuss how to ensure interior sensing systems function reliably in diverse environments and tackle cybersecurity and data privacy concerns in real-time monitoring.

Join this session and get to know more about:

• Identifying the limitations of interior sensing technologies in SDVs and their impact on occupant safety and experience
• Leveraging AI and machine learning to enhance occupant detection, behavior analysis, and system adaptability
• Strategies for sensor fusion, environmental adaptability, and addressing cybersecurity challenges in real-time interior monitoring