论文标题
使用地图和社会环境的城市环境中车辆的行动序列预测
Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context
论文作者
论文摘要
这项工作研究了在现实世界驾驶场景中预测环绕车的未来动作顺序的问题。为此，我们做出了三个主要贡献。第一个贡献是一种自动方法，将在实际驾驶方案中记录的轨迹转换为借助高清图的帮助。该方法可以从大规模驾驶数据中为此任务启用自动数据集创建。我们的第二个贡献在于将方法应用于众所周知的交通代理跟踪和预测数据集，从而产生了228,000个动作序列。此外，手动注释了2,245个动作序列进行测试。第三个贡献是通过将交通代理的过去位置和速度，将信息和社会环境映射到单一端到端可训练的神经网络中，提出一种新颖的动作序列预测方法。我们的实验证明了数据创建方法的优点和创建的数据集的值 - 预测性能随数据集的大小一致，并表明我们的动作预测方法优于比较模型。
This work studies the problem of predicting the sequence of future actions for surround vehicles in real-world driving scenarios. To this aim, we make three main contributions. The first contribution is an automatic method to convert the trajectories recorded in real-world driving scenarios to action sequences with the help of HD maps. The method enables automatic dataset creation for this task from large-scale driving data. Our second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228,000 action sequences. Additionally, 2,245 action sequences were manually annotated for testing. The third contribution is to propose a novel action sequence prediction method by integrating past positions and velocities of the traffic agents, map information and social context into a single end-to-end trainable neural network. Our experiments prove the merit of the data creation method and the value of the created dataset - prediction performance improves consistently with the size of the dataset and shows that our action prediction method outperforms comparing models.
