Vision‐based inter‐vehicle distance estimation for driver alarm system. Issue 6 (26th November 2018)

• Record Type: Journal Article
• Authors: Liu, Zewei; Lu, Dongming; Qian, Weixian; Ren, Kan; Zhang, Jun; Xu, Liwei
• Is Part Of: IET intelligent transport systems. Volume 13:Issue 6(2019)
• Page Start: 927
• Page End: 932
• Publication Date: 2018-11-26
• Subjects: driver information systems -- computer vision -- distance measurement -- object detection -- Kalman filters -- cameras -- alarm systems; vision‐based intervehicle distance estimation -- driver alarm system -- vehicle position determination -- vision‐based driving assistance system -- vertical distance -- horizontal distance -- Kalman framework -- distance measurement -- single monochrome camera -- preceding vehicle detection -- three‐dimensional position estimation -- distance estimation method -- robust real‐time vehicle detection -- driver safety; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2018.5313
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Determining the position of the vehicles on road is an important information in the driver assistance system which is used to increase driver's safety. The authors propose a robust real‐time vehicle detection and distance estimation method for vision‐based driving assistance system. They detect the vehicles in front of their own and estimate their three‐dimensional position by using a single monochrome camera. After detecting the preceding vehicles using the common method, they use a new method for distance measurement based on monocular camera to estimate the inter‐vehicle distance. The method also provides the longitudinal distance information for the driver alarm system. Finally, they utilise the Kalman framework to update the information of the vehicle which they detected before, the information includes horizontal distance, vertical distance and speed, and use these information to predict risk. Experimental results indicate that the system correctly detects vehicles in real time and effectively estimates the position of preceding vehicles.