AI for Safety at Waze, and Its Relation to User Trust in AI Systems

Music City Center 

Road accidents are one of the leading causes of mortality worldwide. This paper, as part of our work at Waze - designs, assesses and deploys a targeted warning system to nudge drivers toward safer behaviors. We develop an end-to-end approach spanning descriptive, predictive and prescriptive analytics. We build a deep learning model to predict accident reports based on historical patterns and contextual information, which we use to develop an indicator of road safety at a granular spatio-temporal level and a global scale. We then design proactive and targeted warnings for users upon entering high-risk road segments. We conduct a large-scale and global randomized controlled trial to evaluate the impact of these warnings. Results show (i) a statistically significant decrease in average speeds and overspeeding rates; (ii) a fatigue effect motivating a parsimonious nudging system; and (iii) heterogeneous responses across drivers—notably, the effect is stronger for users which tend to trust the Waze navigation system more, as indicated by their measurement of past adherence to navigation instructions. The positive results from the experiment led to the global deployment of the targeted warning system, highlighting the role of digital platforms and artificial intelligence to improve road safety worldwide.

Deep Learning

Traffic Safety