LED flicker is a persistent artifact in imaging, where lights modulated via Pulse Width Modulation (PWM) above 90 Hz appear steady to humans but produce temporal intensity variations in captured video. While hardware mitigations like split-pixel architectures reduce flicker, they introduce a fundamental trade-off with motion blur. Progress in learned LED flicker mitigation (LFM) is currently hindered by a lack of public ground-truth datasets. We address this gap with ISET-LFM, an open-source physics-based simulation framework that models LED flicker in driving scenes. Built on the ISET ecosystem, our pipelinecombines camera motion simulation with an analytical flicker model to generate realistic dual-exposure frame sequences alongsideflicker-free ground truth. We provide a synthetic datasetof scene radiance, enabling benchmarking and training of LFMalgorithms across diverse sensor and ISP architectures. Thecode and dataset are available at: https: // github. com/ AyushJam/ iset-lfm and https: // purl. stanford. edu/ wd776hn7919 respectively.