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. 2023 Jul 7;23(13):6238.
doi: 10.3390/s23136238.

Research on Road Scene Understanding of Autonomous Vehicles Based on Multi-Task Learning

Jinghua Guo  1 , Jingyao Wang  2 , Huinian Wang  1 , Baoping Xiao  1 , Zhifei He  1 , Lubin Li  1
Affiliations

Research on Road Scene Understanding of Autonomous Vehicles Based on Multi-Task Learning

Jinghua Guo et al. Sensors (Basel). .

Abstract

Road scene understanding is crucial to the safe driving of autonomous vehicles. Comprehensive road scene understanding requires a visual perception system to deal with a large number of tasks at the same time, which needs a perception model with a small size, fast speed, and high accuracy. As multi-task learning has evident advantages in performance and computational resources, in this paper, a multi-task model YOLO-Object, Drivable Area, and Lane Line Detection (YOLO-ODL) based on hard parameter sharing is proposed to realize joint and efficient detection of traffic objects, drivable areas, and lane lines. In order to balance tasks of YOLO-ODL, a weight balancing strategy is introduced so that the weight parameters of the model can be automatically adjusted during training, and a Mosaic migration optimization scheme is adopted to improve the evaluation indicators of the model. Our YOLO-ODL model performs well on the challenging BDD100K dataset, achieving the state of the art in terms of accuracy and computational efficiency.

Keywords: autonomous vehicles; drivable area detection; lane line detection; multi-task learning; traffic object detection; visual perception.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Our road detection.
Figure 2
Figure 2
Structure of the proposed YOLO-ODL model.
Figure 3
Figure 3
Data augmentation.
Figure 4
Figure 4
Migration optimization.
Figure 5
Figure 5
Detection results of YOLO-ODL in multi-weather road conditions.
Figure 6
Figure 6
Detection results of YOLO-ODL in multi-scenario road conditions.
Figure 7
Figure 7
Comparison of YOLO-ODL and YOLOP object detection results. The red box in the figure is the false detection box, and the yellow box is the missed detection box.
Figure 8
Figure 8
Comparison of YOLO-ODL and YOLOP drivable area detection results. The red circle in the figure is the false detection area, and the yellow circle is the missed detection area.
Figure 9
Figure 9
Comparison of YOLO-ODL and YOLOP lane line detection results. The yellow circle is the missed detection area.
See this image and copyright information in PMC

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