With the growing popularity of digital twin and autonomous driving in transportation, the
demand for simulation systems capable of generating high-fidelity and reliable scenarios is …

High-quality traffic flow generation is the core module in building simulators for autonomous
driving. However, the majority of available simulators are incapable of replicating traffic …

Traffic simulation is a crucial tool for transportation decision-making and policy development.
However, achieving realistic simulations in the face of the high dimensionality and …

Symbiotic simulation systems that incorporate data-driven methods (such as machine/deep
learning) are effective and efficient tools for just-in-time (JIT) operational decision making …

Recent failures in real-world self-driving tests have suggested a paradigm shift from directly
learning in real-world roads to building a high-fidelity driving simulator as an alternative …

Autonomous vehicles (AVs) have the potential to significantly revolutionize society by
providing a secure and efficient mode of transportation. Recent years have witnessed …

Traffic simulation provides interactive data for the optimization of traffic policies. However,
existing traffic simulators are limited by their lack of scalability and shortage in input data …

Virtual traffic benefits a variety of applications, including video games, traffic engineering,
autonomous driving, and virtual reality. To date, traffic visualization via different simulation …

Recent topics of interest such as smart cities and autonomous driving are currently in focus
of many research activities. In this context, simulations are used to evaluate new algorithms …

The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …