|Table of Contents|

Multi-task perception algorithm of autonomous driving based on temporal fusion(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2021年04期
Page:
223-234
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Multi-task perception algorithm of autonomous driving based on temporal fusion
Author(s):
LIU Zhan-wen1;  FAN Song-hua1;  QI Ming-yuan1;  DONG Ming1;  WANG Pin2;  ZHAO Xiang-mo1
(1. School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; 2. University of California, Berkeley, Berkeley 94804-4648, California, USA)
Keywords:
traffic information engineering;  environmental perception;  temporal fusion;  object detection;  semantic segmentation
PACS:
U491.2
DOI:
10.19818/j.cnki.1671-1637.2021.04.017
Abstract:
The sequential image frames were used as input to mine the temporal associated information among the continuous image frames, and a multi-task joint driving environment perception algorithm fusing the temporal information was constructed to rapidly detect the traffic participation targets and drivable area through multi-task supervision and joint optimization. ResNet50 was used as the backbone network, in which a cascaded feature fusion module was built to capture the non-local remote dependence among different image frames. The high-resolution images were processed by the convolution subsampling to accelerate the feature extraction process of different image frames, balancing the detection accuracy and speed of the algorithm. In order to eliminate the influence of spatial displacements of the objects among the image frames on the feature fusion, and considering the non-local dependence of the features of different image frames, the temporal feature fusion module was constructed to align and match the time sequences of feature maps corresponding to different image frames for forming the integrated global feature. Based on the parameter-sharing backbone network, the heat map of generating key point was exploited to detect the positions of pedestrians, vehicles and traffic signal lights on the road, and the semantic segmentation sub-network was built to provide the drivable area information for autonomous vehicles on the road. Analysis results show that the proposed algorithm takes sequential frames as input instead of single frame, which makes effective use of the temporal characteristics of the frames. In addition, its computational complexity with the cascaded feature fusion module greatly reduces to sixteenth of that without the cascaded feature fusion module through downsampling. Compared with other mainstream models, such as CornerNet and ICNet, the detection accuracy and segmentation performance of the algorithm improve by an average of 6% and 5%, respectively, and the image processing speed reaches to 12 frames per second. Therefore, the proposed algorithm has obvious advantages in the speed and accuracy of image detection and segmentation. 6 tabs, 9 figs, 31 refs.

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Last Update: 2021-09-01