MIDAR: Mimicking LiDAR Detection for Traffic Applications with a Lightweight Plug-and-Play Model

As autonomous driving (AD) technology advances, increasing research has focused on leveraging cooperative perception (CP) data collected from multiple AVs to enhance traffic applications. Due to the impracticality of large-scale real-world AV deployments, simulation has become the primary approach in most studies. While game-engine-based simulators like CARLA generate high-fidelity raw sensor data (e.g., LiDAR point clouds) which can be used to produce realistic detection outputs, they face scalability challenges in multi-AV scenarios. In contrast, microscopic traffic simulators such as SUMO scale efficiently but lack perception modeling capabilities. To bridge this gap, we propose MIDAR, a LiDAR detection mimicking model that approximates realistic LiDAR detections using vehicle-level features readily available from microscopic traffic simulators. Specifically, MIDAR predicts true positives (TPs) and false negatives (FNs) from ideal LiDAR detection results based on the spatial layouts and dimensions of surrounding vehicles. A Refined Multi-hop Line-of-Sight (RM-LoS) graph is constructed to encode the occlusion relationships among vehicles, upon which MIDAR employs a GRU-enhanced APPNP architecture to propagate features from the ego AV and occluding vehicles to the prediction target. MIDAR achieves an AUC of 0.909 in approximating the detection results generated by CenterPoint, a mainstream 3D LiDAR detection model, on the nuScenes AD dataset. Two CP-based traffic applications further validate the necessity of such realistic detection modeling, particularly for tasks requiring accurate individual vehicle observations (e.g., position, speed, lane index). As demonstrated in the applications, MIDAR can be seamlessly integrated into traffic simulators and trajectory datasets and will be open-sourced upon publication.