PADSIM: A Photorealistic Closed-Loop Simulator with Controllable Pedestrians for Autonomous Driving

Closed-loop simulation is essential for evaluating autonomous driving systems (ADS) in safety-critical urban scenarios. However, existing simulators primarily focus on vehicle-centric behaviors and largely overlook the impact of pedestrian dynamics on decision-making. To address this limitation, we present $\text{PADSIM}$, a photorealistic closed-loop simulator that enables controllable and interaction-consistent pedestrian behaviors in dynamic environments. PADSIM supports flexible placement and motion control of pedestrians while ensuring physically plausible and collision-free interactions with surrounding agents. At the core of our system is an Interaction-Driven Motion Generation (IDMG) pipeline, which produces scene-aware pedestrian trajectories by explicitly modeling multi-agent interactions and incorporating physical constraints. To maintain visual realism, we further introduce a Global-Local Illumination-Consistent Synthesis (GLICS) strategy that preserves human–scene appearance consistency by leveraging environmental lighting cues without additional optimization or training. By tightly coupling motion generation, rendering, and policy inference in a recurrent loop, PADSIM enables reactive simulation beyond open-loop replay. In addition, we propose a system-level complexity metric to quantify the risk and difficulty of generated scenarios from the ego vehicle’s perspective. Experiments on real-world datasets demonstrate that PADSIM supports realistic augmentation of pedestrian behaviors, facilitates policy-driven scenario evolution, and enables more comprehensive evaluation of autonomous driving systems under safety-critical human–vehicle interactions.