Vehicle light detection is required for important downstream safe autonomous driving tasks, such as predicting a vehicle's light state to determine if the vehicle is making a lane change or turning. Currently, many vehicle light detectors use single-stage detectors which predict bounding boxes to identify a vehicle light, in a manner decoupled from vehicle instances. In this paper, we present a method for detecting a vehicle light given an upstream vehicle detection and approximation of a visible light's center. Our method predicts four approximate corners associated with each vehicle light. We experiment with CNN architectures, data augmentation, and contextual preprocessing methods designed to reduce surrounding-vehicle confusion. We achieve an average distance error from the ground truth corner of 5.09 pixels, about 17.24% of the size of the vehicle light on average. We train and evaluate our model on the LISA Lights dataset, allowing us to thoroughly evaluate our vehicle light corner detection model on a large variety of vehicle light shapes and lighting conditions. We propose that this model can be integrated into a pipeline with vehicle detection and vehicle light center detection to make a fully-formed vehicle light detection network, valuable to identifying trajectory-informative signals in driving scenes.

翻译：车辆灯光检测对于下游安全自动驾驶任务至关重要，例如通过预测车辆灯光状态判断车辆是否变道或转弯。当前许多车灯检测器采用单阶段检测器以独立于车辆实例的方式预测边界框来识别车灯。本文提出一种方法，在给定上游车辆检测及可见灯光中心近似值的基础上，预测与每个车灯关联的四个近似角点。我们通过CNN架构、数据增强及上下文预处理方法减少周围车辆干扰。模型在真实角点的平均距离误差为5.09像素，约为车灯平均尺寸的17.24%。我们利用LISA Lights数据集训练并评估模型，全面验证其对多种车灯形状与光照条件的角点检测性能。该模型可集成至包含车辆检测与车灯中心检测的流水线中，形成完整的车灯检测网络，用于识别驾驶场景中的轨迹信息信号。