Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction

• URL: http://arxiv.org/abs/2108.05814v1
• Date: Thu, 12 Aug 2021 15:53:37 GMT
• Title: Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction
• Authors: Edoardo Mello Rella (1), Jan-Nico Zaech (1), Alexander Liniger (1), Luc Van Gool (1 and 2) ((1) Computer Vision Lab, ETH Z\"uurich (2) PSI, KU Leuven)
• Abstract summary: We propose a recurrent, attention-based approach for motion forecasting. Decoder Fusion RNN (DF-RNN) is composed of a recurrent behavior encoder, an inter-agent multi-headed attention module, and a context-aware decoder. We demonstrate the efficacy of our method by testing it on the Argoverse motion forecasting dataset and show its state-of-the-art performance on the public benchmark.
• Score: 53.473846742702854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe and reliable autonomous driving systems. It is a challenging problem as agents adjust their behavior depending on their intentions, the others' actions, and the road layout. In this paper, we propose Decoder Fusion RNN (DF-RNN), a recurrent, attention-based approach for motion forecasting. Our network is composed of a recurrent behavior encoder, an inter-agent multi-headed attention module, and a context-aware decoder. We design a map encoder that embeds polyline segments, combines them to create a graph structure, and merges their relevant parts with the agents' embeddings. We fuse the encoded map information with further inter-agent interactions only inside the decoder and propose to use explicit training as a method to effectively utilize the information available. We demonstrate the efficacy of our method by testing it on the Argoverse motion forecasting dataset and show its state-of-the-art performance on the public benchmark.

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