2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-48.2
Paper Title F-NET: FUSION NEURAL NETWORK FOR VEHICLE TRAJECTORY PREDICTION IN AUTONOMOUS DRIVING
Authors Jue Wang, Peking University / Tencent Technology (Beijing) Company Limited, China; Ping Wang, Chao Zhang, Peking University, China; Kuifeng Su, Tencent Company, China; Jun Li, University of Chinese Academy of Sciences, China
SessionMLSP-48: Neural Network Applications
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent research has been remarkable in recurrent neural networks (RNNs) on sequence-to-sequence problems for image caption, and promising in convolutional neural networks (CNNs) on spatial analysis problems for image detection and sematic segmentation problems. In this paper, based on recurrent neural networks and convolutional neural networks, we propose a fusion neural network architecture named F-Net to deal with vehicle trajectory prediction on highway and urban scenarios in autonomous driving applications. The novelty of the proposed method is the attention mechanism that affects effectively in the progress of both RNN and CNN feature extraction. Besides, our sufficient usage of raw sensor data protects scene texture information of environment and interaction among surrounding vehicles. Experimental results on the nuScene dataset show that our proposed method outperforms the state-of-the-art methods.