Incorporating Ephemeral Traffic Waves in A Data-Driven Framework for Microsimulation in CARLA
🤖 AI Summary
This study addresses the limitation of conventional microsimulation in faithfully reproducing transient traffic wave dynamics. We propose a data-driven co-simulation framework integrating CARLA with high-fidelity trajectory data from the I-24 MOTION dataset. Our method introduces a boundary-condition-driven mechanism—leveraging ghost cells, autonomous vehicle generation, and configurable vehicle dynamics models—to reconstruct spatiotemporal traffic states end-to-end. To our knowledge, this is the first implementation of measurement-based, boundary-driven simulation within CARLA, overcoming the constraints of localized car-following models and enabling microscale emergence of macroscopic traffic phenomena. Experiments successfully replicate the formation, propagation, and dissipation of traffic waves under both high- and low-density conditions, achieving significantly improved spatiotemporal fidelity. The framework provides a high-fidelity simulation environment for evaluating traffic control strategies and validating autonomous vehicle perception systems.
Application Category
📝 Abstract
This paper introduces a data-driven traffic microsimulation framework in CARLA that reconstructs real-world wave dynamics using high-fidelity time-space data from the I-24 MOTION testbed. Calibration of road networks in microsimulators to reproduce ephemeral phenomena such as traffic waves for large-scale simulation is a process that is fraught with challenges. This work reconsiders the existence of the traffic state data as boundary conditions on an ego vehicle moving through previously recorded traffic data, rather than reproducing those traffic phenomena in a calibrated microsim. Our approach is to autogenerate a 1 mile highway segment corresponding to I-24, and use the I-24 data to power a cosimulation module that injects traffic information into the simulation. The CARLA and cosimulation simulations are centered around an ego vehicle sampled from the empirical data, with autogeneration of "visible" traffic within the longitudinal range of the ego vehicle. Boundary control beyond these visible ranges is achieved using ghost cells behind (upstream) and ahead (downstream) of the ego vehicle. Unlike prior simulation work that focuses on local car-following behavior or abstract geometries, our framework targets full time-space diagram fidelity as the validation objective. Leveraging CARLA's rich sensor suite and configurable vehicle dynamics, we simulate wave formation and dissipation in both low-congestion and high-congestion scenarios for qualitative analysis. The resulting emergent behavior closely mirrors that of real traffic, providing a novel cosimulation framework for evaluating traffic control strategies, perception-driven autonomy, and future deployment of wave mitigation solutions. Our work bridges microscopic modeling with physical experimental data, enabling the first perceptually realistic, boundary-driven simulation of empirical traffic wave phenomena in CARLA.
Problem
Research questions and friction points this paper is trying to address.
Reconstructing real-world traffic wave dynamics using high-fidelity empirical data
Calibrating microsimulators to reproduce ephemeral traffic phenomena at scale
Creating perceptually realistic simulations of traffic wave formation and dissipation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Data-driven traffic microsimulation using real-world wave dynamics
Cosimulation module injects traffic data into CARLA
Boundary control with ghost cells for realistic simulation
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