New tracing platform pinpoints delays across complex autonomous-driving software
Peer-Reviewed Publication
Updates every hour. Last Updated: 21-Oct-2026 10:17 ET (21-Oct-2026 14:17 GMT/UTC)
“I work with data, but what truly motivates me is knowing that it will ultimately be of use to someone. I’m not particularly good at taking on a leadership role, so I prefer to work alongside others, ” says Professor Shinichiroh Yokota from the Graduate School of Nursing. With a background as a nurse in clinical practice, Professor Yokota now applies informatics methods to nursing and medical data—using tools such as machine learning on electronic medical records (EMRs) to predict patient falls and the risk of bedsores. We spoke with him about how AI is transforming the world of nursing.
To simulate event cameras, which can capture brightness changes at microsecond-scale speeds, conventional methods render numerous frames from 3D scenes, resulting in high computational costs. Researchers from Japan developed an efficient method using physically based path tracing to precisely determine event timings. The efficient implementation using GPUs reduces computation time to as little as one-third of a naïve implementation, enabling more efficient training-data generation for event camera applications, including healthcare monitoring, autonomous driving, and robotics.