Over the last few decades, the prevalence of drug-induced fatal car accidents has drastically increased. In the US, for example, the National Highway Traffic Safety Administration (NHTSA) reports that 56% of the injured or killed roadway participants were tested positive. Thereby, the most prevalent drug was cannabis with 25%. Since the legalization of cannabis is currently globally progressing, urgent measures are needed to prevent a highly likely increase of such accidents.

In light of these challenges, we are looking for a personality with a strong will to create a meaningful impact. Our goal is to design, implement, and evaluate a cannabis-impaired driving detection and prevention system. Building upon previous studies that have been well-recognized for their contributions (ETH medal, CHI Best Paper Award), we will first conduct a real-world study in which subjects are put under the influence of cannabis while performing a pre-driving test and a driving task on a test track. We then develop and evaluate a robust cannabis-driving detection model before and while driving. Ultimately, we are working towards a scalable digital biomarker platform for in-vehicle driver state detection, contributing to the long-term goal of Vision Zero (eliminating traffic-related fatalities and severe injuries).