For automated driving, predicting the future trajectories of other road users in complex traffic situations is a hard problem. Modern neural networks use the past trajectories of traffic participants as well as map data to gather hints about the possible driver intention and likely maneuvers. With increasing connectivity between cars and other traffic actors, cooperative information is another source of data that can be used as inputs for trajectory prediction algorithms. Connected actors might transmit their intended path or even complete planned trajectories to other actors, which simplifies the prediction problem due to the imposed constraints. In this work, we outline the benefits of using this source of data for trajectory prediction and propose a graph-based neural network architecture that can leverage this additional data. We show that the network performance increases substantially if cooperative data is present. Also, our proposed training scheme improves the network's performance even for cases where no cooperative information is available. We also show that the network can deal with inaccurate cooperative data, which allows it to be used in real automated driving environments.

翻译：自动驾驶中，预测复杂交通场景下其他道路使用者的未来轨迹是一个难题。现代神经网络利用交通参与者的历史轨迹以及地图数据，来获取可能驾驶员意图和潜在机动动作的线索。随着车辆与其他交通参与者之间的互联性增强，协作信息成为轨迹预测算法可用的另一种数据来源。互联的参与者可能将自身预定路径甚至完整规划轨迹传输给其他参与者，由于引入了约束条件，这简化了预测问题。本文概述了利用这一数据源进行轨迹预测的优势，并提出了一种能够利用这种额外数据的基于图的神经网络架构。我们证明，当存在协作数据时，网络性能显著提升。此外，我们提出的训练方案即使在无法获取协作信息的情况下，也能改善网络性能。我们还证明，该网络能够处理不准确的协作数据，使其适用于真实的自动驾驶环境。