Recurrent Neural Network Architectures for Vulnerable Road User Trajectory Prediction

Hui Xiong1, Fabian Flohr2, Sijia Wang1, Baofeng Wang3, Jianqiang Wang1, Keqiang Li1

  • 1Tsinghua University
  • 2Daimler AG
  • 3Research and Development of Autonomous Driving & Safety, Daimler

Details

Category

Workshop

Sessions

09:00 - 13:00 | Sun 9 Jun | Room L118 | SuET7

DDIVA: Data Driven Intelligent Vehicle Applications

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Abstract

We present an experimental study comparing various Recurrent Neural Network architectures for the task of Vulnerable Road User (VRU) motion trajectory prediction in the intelligent vehicle domain. Making use of temporal motion cues and visual appearance features, we design multi-cue RNN-based architectures with dedicated optimization process to predict future moving trajectories from historical consecutive frames. Experiments are performed on image sequences recorded from on-board a moving vehicle and public tracking datasets. In particular, the Tsinghua-Daimler Cyclist Benchmark (TDCB) has been augmented with additional annotations (various VRU types) to support the evaluation of object tracking approaches and trajectory prediction methods. This newly introduced dataset is termed TDCB-Track. We demonstrate the effectiveness of the proposed RNN architectures on the public MOT16 dataset and the TDCB-Track dataset. We show that the proposed approaches outperform simpler baseline methods and stay ahead with the state-of-the-art.

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