Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context

• URL: http://arxiv.org/abs/2004.14251v1
• Date: Wed, 29 Apr 2020 14:59:58 GMT
• Title: Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context
• Authors: Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Luc Van Gool
• Abstract summary: This work studies the problem of predicting the sequence of future actions for surround vehicles in real-world driving scenarios. 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 second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228,000 action sequences. 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
• Score: 152.0714518512966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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