Technology and application of intelligent driving based on visual perception. Issue 3 (25th April 2019)

• Record Type: Journal Article
• Authors: Zhang, Xinyu; Gao, Hongbo; Xie, Guotao; Gao, Buyun; Li, Deyi
• Is Part Of: CAAI transactions on intelligence technology. Volume 2:Issue 3(2017)
• Page Start: 126
• Page End: 132
• Publication Date: 2019-04-25
• Subjects: image sensors -- image segmentation -- feature extraction -- robot vision -- sensors -- object detection -- SLAM (robots) -- road vehicles -- traffic engineering computing -- intelligent transportation systems; visual perception -- intelligent driving environment -- obstacle detection -- traffic sign detection -- visual sensor model -- installation location -- visual sensor information processing module -- intelligent driving system software modules -- software architecture -- driving brain -- visual sensors -- target segment -- image segmentation algorithm -- segmentation region -- intelligent driving decision -- multivision sensors -- intelligent driving hardware experimental platform -- intelligent driving hardware test platforms -- visual simultaneous localisation and mapping -- camera; B6135 Optical, image and video signal processing -- C3390C Mobile robots -- C5260B Computer vision and image processing techniques -- C7445 Traffic engineering computing; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2017.0015
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : The camera is one of the important sensors to realise the intelligent driving environment. It can realise lane detection and tracking, obstacle detection, traffic sign detection, identification and discrimination and visual simultaneous localisation and mapping. The visual sensor model, quantity and installation location are different on different intelligent driving hardware experimental platform as well as the visual sensor information processing module, thus a number of intelligent driving system software modules and interfaces are different. In this study, the software architecture of the autonomous vehicle based on the driving brain is used to adapt to different types of visual sensors. The target segment is extracted by the image segmentation algorithm, and then the segmentation of the region of interest is carried out. According to the input feature calculation results, the obstacle search is done in the second segmentation region, the output of the accessible road area. As driving information is complete, the authors will increase or reduce one or more visual sensors, change the visual sensor model or installation location, which will no longer directly affect the intelligent driving decision, they make the multi‐vision sensors adapted to the requirements of different intelligent driving hardware test platforms.