TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913813319647232 |
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| author | Polley, Nikolai Pavlitska, Svetlana Boualili, Yacin Rohrbeck, Patrick Stiller, Paul Bangaru, Ashok Kumar Zöllner, J. Marius |
| author_facet | Polley, Nikolai Pavlitska, Svetlana Boualili, Yacin Rohrbeck, Patrick Stiller, Paul Bangaru, Ashok Kumar Zöllner, J. Marius |
| contents | Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. Utilizing a comprehensive dataset amalgamation, including the Bosch Small Traffic Lights Dataset, LISA, the DriveU Traffic Light Dataset, and a proprietary dataset from Karlsruhe, we ensure a robust evaluation across varied scenarios. Furthermore, we propose a relevance estimation system that innovatively uses directional arrow markings on the road, eliminating the need for prior map creation. On the DriveU dataset, this approach results in 96% accuracy in relevance estimation. Finally, a real-world evaluation is performed to evaluate the deployment and generalizing abilities of these models. For reproducibility and to facilitate further research, we provide the model weights and code: https://github.com/KASTEL-MobilityLab/traffic-light-detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07284 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis Polley, Nikolai Pavlitska, Svetlana Boualili, Yacin Rohrbeck, Patrick Stiller, Paul Bangaru, Ashok Kumar Zöllner, J. Marius Computer Vision and Pattern Recognition Machine Learning Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. Utilizing a comprehensive dataset amalgamation, including the Bosch Small Traffic Lights Dataset, LISA, the DriveU Traffic Light Dataset, and a proprietary dataset from Karlsruhe, we ensure a robust evaluation across varied scenarios. Furthermore, we propose a relevance estimation system that innovatively uses directional arrow markings on the road, eliminating the need for prior map creation. On the DriveU dataset, this approach results in 96% accuracy in relevance estimation. Finally, a real-world evaluation is performed to evaluate the deployment and generalizing abilities of these models. For reproducibility and to facilitate further research, we provide the model weights and code: https://github.com/KASTEL-MobilityLab/traffic-light-detection. |
| title | TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.07284 |