Yang L, Lei W, Zhang W, Ye T. Dual-flow network with attention for autonomous driving.
Front Neurorobot 2023;
16:978225. [PMID:
36699946 PMCID:
PMC9868693 DOI:
10.3389/fnbot.2022.978225]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023] Open
Abstract
We present a dual-flow network for autonomous driving using an attention mechanism. The model works as follows: (i) The perception network extracts red, blue, and green (RGB) images from the video at low speed as input and performs feature extraction of the images; (ii) The motion network obtains grayscale images from the video at high speed as the input and completes the extraction of object motion features; (iii) The perception and motion networks are fused using an attention mechanism at each feature layer to perform the waypoint prediction. The model was trained and tested using the CARLA simulator and enabled autonomous driving in complex urban environments, achieving a success rate of 74%, especially in the case of multiple dynamic objects.
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