arXiv:2606. 03823v1 Announce Type: new Abstract: Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric vehicle charging stations.
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arXiv:2608. 13993v1 Announce Type: new Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations.
arXiv:2405. 17468v3 Announce Type: replace-cross Abstract: Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations.
REARL is a closed‑loop simulation enhancement framework that combines real traffic data with large language models (LLMs) to improve autonomous driving simulations. It clusters real traffic, uses cluster centers as representative scenarios for the LLM, and employs a sliding‑window detector to monitor vehicle speed and spacing discrepancies. When thresholds are exceeded, the LLM adjusts vehicle decision‑making or selects matching real vehicle actions, resulting in lower Hellinger distance and MAPE compared to baselines in a HighD highway setting.
The paper introduces the Markov Chain Car‑Following (MC‑CF) model, an empirical probabilistic approach that represents car‑following as a Markov process and samples accelerations from empirical distributions within discretized state bins. Evaluation on the Waymo Open Motion Dataset shows that MC‑CF variants outperform physics‑based baselines and compete with modern data‑driven methods in both one‑step and open‑loop trajectory prediction. Zero‑shot transfer to the Naturalistic Phoenix dataset and microscopic ring‑road simulations demonstrate cross‑domain generalization and scalability, with the model reducing collisions and reproducing naturalistic shockwave propagation.
arXiv:2606. 02287v1 Announce Type: cross Abstract: Urban trajectory generation is a fundamental task for transportation simulation, urban planning, and mobility analytics.
arXiv:2408.15538v4 Announce Type: replace Abstract: While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with...