Your browser doesn't support javascript.
loading
Machine vision-based autonomous road hazard avoidance system for self-driving vehicles.
Qiu, Chengqun; Tang, Hao; Yang, Yuchen; Wan, Xinshan; Xu, Xixi; Lin, Shengqiang; Lin, Ziheng; Meng, Mingyu; Zha, Changli.
Afiliación
  • Qiu C; School of Automotive Engineering, Yancheng Institute of Technology, Yancheng, 224051, China.
  • Tang H; Jiangsu Province Intelligent Optoelectronic Devices and Measurement-Control Engineering Research Center, Yancheng Teachers University, Yancheng, 224007, China.
  • Yang Y; School of Automotive Engineering, Yancheng Institute of Technology, Yancheng, 224051, China.
  • Wan X; Jiangsu Province Intelligent Optoelectronic Devices and Measurement-Control Engineering Research Center, Yancheng Teachers University, Yancheng, 224007, China.
  • Xu X; Jiangsu Province Intelligent Optoelectronic Devices and Measurement-Control Engineering Research Center, Yancheng Teachers University, Yancheng, 224007, China.
  • Lin S; School of Automotive Engineering, Yancheng Institute of Technology, Yancheng, 224051, China.
  • Lin Z; School of Automotive Engineering, Yancheng Institute of Technology, Yancheng, 224051, China.
  • Meng M; School of Automotive and Transportation Engineering, Hefei University of Technology, Anhui, 230009, China.
  • Zha C; Interdisciplinary Graduate School of Science & Engineering, Tokyo Institute of Technology, Yokohama, 2268502, Japan.
Sci Rep ; 14(1): 12178, 2024 May 28.
Article en En | MEDLINE | ID: mdl-38806585
ABSTRACT
The resolution of traffic congestion and personal safety issues holds paramount importance for human's life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm's backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido