Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicenter of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output and ranks them according to probability. To verify the method’s performance and generalizability, we present a real-world dataset that consists of over 23 000 vehicles traversing five different intersections, collected using a vehicle-mounted lidar-based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.

中文翻译：

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使用循环神经网络的自然驾驶员意图和路径预测

了解驾驶员在十字路口的意图是自动驾驶汽车的关键组成部分。没有交通信号的城市十字路口是高度可变的车辆运动和交互的常见中心。我们提出了一种通过具有不确定性的多模态轨迹预测来预测城市交叉路口驾驶员意图的方法。我们的方法基于与混合密度网络输出层相结合的循环神经网络。为了巩固输出概率分布的多模态特性，我们引入了一种聚类算法，该算法提取存在于预测输出中的一组可能路径，并根据概率对它们进行排序。为了验证该方法的性能和通用性，我们展示了一个真实世界的数据集，其中包含超过 23 000 辆穿越五个不同十字路口的车辆，这些数据集是使用车载激光雷达跟踪系统收集的。一系列指标用于展示模型针对多个基线的性能。