How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912432973152256 |
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| author | Tan, Kaiyuan N, Pavan Kumar B Chakravarthi, Bharatesh |
| author_facet | Tan, Kaiyuan N, Pavan Kumar B Chakravarthi, Bharatesh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection Tan, Kaiyuan N, Pavan Kumar B Chakravarthi, Bharatesh Computer Vision and Pattern Recognition 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. |
| title | How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.13722 |