Mobile app-based study of driving behaviors under the influence of cannabis

Cannabis use has become increasingly prevalent due to evolving legal and societal attitudes, raising concerns about its influence on public safety, particularly in driving. Existing studies mostly rely on simulators or specialized equipment, which do not capture the complexities of real-world driving and pose cost and scalability issues. In this paper, we investigate the effects of cannabis on driving behavior using participants’ smartphones to gather data in natural settings. Our method focuses on three critical behaviors: weaving & swerving, wide turning, and hard braking. We propose a two-step segmentation algorithm for processing continuous motion sensor data and use threshold-based methods for efficient detection. A custom application autonomously records driving events during actual road scenarios. On-road experiments with 9 participants who consumed cannabis under controlled conditions reveal a correlation between cannabis use and altered driving behaviors, with significant effects emerging approximately 2$\sim$3 h after consumption.