How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection

• URL: http://arxiv.org/abs/2506.13722v1
• Date: Mon, 16 Jun 2025 17:27:43 GMT
• Title: How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection
• Authors: Kaiyuan Tan, Pavan Kumar B N, Bharatesh Chakravarthi,
• Abstract summary: Event cameras are well-suited for real-time object detection at traffic intersections.<n>The development of robust event-based detection models is hindered by the limited availability of annotated real-world datasets.<n>This study offers the first quantifiable analysis of the sim-to-real gap in event-based object detection using CARLAs DVS.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event cameras are gaining traction in traffic monitoring applications due to their low latency, high temporal resolution, and energy efficiency, which makes them well-suited for real-time object detection at traffic intersections. However, the development of robust event-based detection models is hindered by the limited availability of annotated real-world datasets. To address this, several simulation tools have been developed to generate synthetic event data. Among these, the CARLA driving simulator includes a built-in dynamic vision sensor (DVS) module that emulates event camera output. Despite its potential, the sim-to-real gap for event-based object detection remains insufficiently studied. In this work, we present a systematic evaluation of this gap by training a recurrent vision transformer model exclusively on synthetic data generated using CARLAs DVS and testing it on varying combinations of synthetic and real-world event streams. Our experiments show that models trained solely on synthetic data perform well on synthetic-heavy test sets but suffer significant performance degradation as the proportion of real-world data increases. In contrast, models trained on real-world data demonstrate stronger generalization across domains. This study offers the first quantifiable analysis of the sim-to-real gap in event-based object detection using CARLAs DVS. Our findings highlight limitations in current DVS simulation fidelity and underscore the need for improved domain adaptation techniques in neuromorphic vision for traffic monitoring.

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