新模型无需地图即可精准判断红绿灯相关性，适合真实道路部署。
TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis
- 用道路箭头方向判断红绿灯相关性，免去预先建图
- 在DriveU数据集上相关性识别准确率达96%
- 提供完整代码和权重，支持复现与进一步研究
高效交通灯检测是自动驾驶感知系统的关键组件。本文提出一种新型深度学习检测系统，克服以往方法的挑战。通过整合Bosch小交通灯数据集、LISA、DriveU交通灯数据集以及卡尔斯鲁厄的专有数据集，实现多样化场景下的稳健评估。此外，提出一种创新的相关性估计系统，利用道路方向箭头标记，无需预先地图即可判断交通灯相关性，在DriveU数据集上达到96%准确率。最后进行真实世界评估，检验模型的部署与泛化能力。为保证可复现性并推动后续研究，已公开模型权重与代码：https://github.com/KASTEL-MobilityLab/traffic-light-detection。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。