Understanding Drivers’ Visual and Comprehension Loads in Traffic Violation Hotspots Leveraging Crowd-Based Driving Simulation

Traffic violations have become one of the major threats to urban transportation systems, undermining road safety and causing economic losses. Although various methods have been proposed by road authorities and researchers to find out the possible causes of traffic violations, existing methods often fail to diagnose traffic violations from drivers’ perspectives and contexts or consider their visual and comprehension loads while driving. In this work, we propose a driver-centered simulation platform to inspect drivers’ loads in traffic violation hotspots. Specifically, we first build a driving simulator based on the 3D point clouds of real-world traffic violation hotspots. We then recruit drivers to simulate driving in designated traffic scenes. Indicators for drivers’ visual and comprehension loads are derived based on drivers’ feedback. Upon this basis, we build an explainable model to automatically indicate drivers’ visual and comprehension loads under various crowd-sensed traffic scenes. Experiments using real-world data from a Chinese City (Xiamen) and case studies show that our approach successfully derives a set of prominent indicators to effectively diagnose drivers’ visual and comprehension loads in real-world traffic violation hotspots.