InterSim: Innovative Intersection Digital Twins for Human-in-the-Loop Driving Simulation
Luyang Jiang, Chuheng Wei, Yaguang Li, Shaoqi Wang, Yupeng Zhou, Hyunsoo Yun, Xishun Liao, Brandon Pitts 等 9 位作者
Innovative intersection designs can improve roadway safety and operational efficiency, but their uncommon geometries may create challenges for driver comprehension during early deployment. Evaluating these designs through full-scale field implementation can also be costly. This paper presents InterSim, a Unity game engine-based human-in-the-loop (HITL) driving simulator that constructs digital twins of site-specific innovative intersections in a virtual environment. InterSim links the real world and the simulation world through two closed cycles: a digital twin cycle that builds each intersection twin from agency design and traffic data and returns evidence from simulation to the agency, and an HITL cycle in which human drivers or automated driving algorithms operate the ego vehicle under identical scenario conditions. The research capabilities of InterSim are demonstrated through two case studies: a surrogate safety study compares post-encroachment time distributions between a Reduced Conflict Intersection and a standard intersection under different traffic volumes; an eco-driving study compares human drivers with a rule-based agent using vehicle-to-infrastructure information and reveals differences in emission and driving behavior. These studies demonstrate that InterSim provides a reusable digital twin platform for investigating innovative intersection designs from diverse perspectives. Demo video and open-source code can be accessed at https://purduedigitaltwin.github.io/InterSim/
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