论文标题
联合检测预测模型的不确定性感知车辆方向估计
Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
论文作者
论文摘要
对象检测是自动驾驶系统的关键组成部分，其任务是推断周围交通行为者的当前状态。尽管存在许多关于推断车辆参与者的位置和形状的问题，但了解演员的方向仍然是现有最新检测器的挑战。取向是自治系统下游模块的重要特性，尤其与当前方法挣扎的固定或反向参与者的运动预测有关。我们专注于此任务，并提出一种扩展了执行联合对象检测和运动预测的现有模型的方法，从而使我们更准确地推断了车辆方向。此外，该方法能够量化预测不确定性，从而输出推断方向的概率，从而可以改善运动预测和更安全的自主操作。经验结果表明了该方法的好处，在开源的Nuscenes数据集上获得了最先进的性能。
Object detection is a critical component of a self-driving system, tasked with inferring the current states of the surrounding traffic actors. While there exist a number of studies on the problem of inferring the position and shape of vehicle actors, understanding actors' orientation remains a challenge for existing state-of-the-art detectors. Orientation is an important property for downstream modules of an autonomous system, particularly relevant for motion prediction of stationary or reversing actors where current approaches struggle. We focus on this task and present a method that extends the existing models that perform joint object detection and motion prediction, allowing us to more accurately infer vehicle orientations. In addition, the approach is able to quantify prediction uncertainty, outputting the probability that the inferred orientation is flipped, which allows for improved motion prediction and safer autonomous operations. Empirical results show the benefits of the approach, obtaining state-of-the-art performance on the open-sourced nuScenes data set.
