首个专门标注刹车灯与转向灯的车尾灯数据集，助力自动驾驶理解车辆意图。
TLD: A Vehicle Tail Light signal Dataset and Benchmark
- 构建两阶段检测模型，先定位车辆再识别尾灯状态
- 包含152,000张标注图像，覆盖17.78小时真实驾驶视频
- 首次实现真实场景下刹车灯与转向灯的独立标注，适合智能驾驶研究
理解其他驾驶员的意图对安全驾驶至关重要，而现有自动驾驶系统对尾灯信号的作用重视不足。准确识别尾灯信号对于预测车辆行为、预防碰撞具有重要意义。目前开源的尾灯数据集稀缺，普遍规模小且标注不一致。为此，我们提出一个新的大规模尾灯数据集TLD，涵盖全球范围内的多样交通场景。据我们所知，TLD是首个在真实驾驶场景中分别标注刹车灯与转向灯的数据集。数据来自互联网收集的17.78小时驾驶视频，共包含152,000张以2 Hz采样率标注的图像帧，以及150万张穿插其中的未标注帧。此外，我们开发了一个两阶段车辆灯光检测模型，包含车辆检测器和尾灯分类器两个核心模块：首先利用YOLOv10和DeepSORT在时间序列中捕捉连续车辆图像；随后两个分类器同步判断刹车灯与转向灯状态；最后通过后处理消除误检噪声，输出给定时间段内车辆的尾灯状态。该方法在本数据集上表现优异，为车辆尾灯检测建立了基准。数据集已公开于https://huggingface.co/datasets/ChaiJohn/TLD/tree/main。
原文摘要 · Abstract (English)
Understanding other drivers' intentions is crucial for safe driving. The role of taillights in conveying these intentions is underemphasized in current autonomous driving systems. Accurately identifying taillight signals is essential for predicting vehicle behavior and preventing collisions. Open-source taillight datasets are scarce, often small and inconsistently annotated. To address this gap, we introduce a new large-scale taillight dataset called TLD. Sourced globally, our dataset covers diverse traffic scenarios. To our knowledge, TLD is the first dataset to separately annotate brake lights and turn signals in real driving scenarios. We collected 17.78 hours of driving videos from the internet. This dataset consists of 152k labeled image frames sampled at a rate of 2 Hz, along with 1.5 million unlabeled frames interspersed throughout. Additionally, we have developed a two-stage vehicle light detection model consisting of two primary modules: a vehicle detector and a taillight classifier. Initially, YOLOv10 and DeepSORT captured consecutive vehicle images over time. Subsequently, the two classifiers work simultaneously to determine the states of the brake lights and turn signals. A post-processing procedure is then used to eliminate noise caused by misidentifications and provide the taillight states of the vehicle within a given time frame. Our method shows exceptional performance on our dataset, establishing a benchmark for vehicle taillight detection. The dataset is available at https://huggingface.co/datasets/ChaiJohn/TLD/tree/main
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