Kiyotaka Hirahara

Improved action point model in traffic flow based on driver's cognitive mechanism

Car-following modelling in traffic flow theory has been becoming of increasing importance in traffic engineering and Intelligent Transport System(ITS), the point of concentration in this research field is how to analysis and measurement of driver cognitive behaviour. Based on qualitative description of driving behaviour with the new concept of drivers multi-typed information process and multi-ruled decision-making mechanism, this paper has analysed in more detail the AP (action point) model, and ameliorated AP model by eliminating its deficiency. The emphasis of this paper is placed on the deduction of the acceleration equations by considering that the following car is subjected in congested traffic flow. Furthermore, from the cybernetics perspective, this paper has carried out numeral simulation to car-following behaviour with deceleration and acceleration algorithms. The model validation and simulation results have shown that the improved action point car-following model can replicated car-following behaviour and be able to use to reveal the essence of traffic flow characteristics.

Detection of street-parking vehicles using line scan camera and scanning laser range sensor

Street-parking vehicles in urban areas are causing various traffic problems, however, have been counted manually in the traffic census, including counting error. We propose two auto-detection methods; one is based on an epipolar-plane image analysis, using a line-scan camera, and the other is based on three-dimensional restoration, using a scanning laser range sensor. Both methods achieve high detection rate. Furthermore, we introduce two-type panoramic street images, obtained by vertically line-scanning camera and laser sensors. Street-parking vehicles side-images will be extracted from the panoramic street images.

Street-parking vehicle detection using line scan camera

Street-parking vehicles cause traffic congestions and traffic accidents. Our proposal is developing a new technique for automatic detecting street-parking vehicles. For this, we generated a spatio-temporal image using a line scan camera. The line images are laminated in at accordance with time direction and an epipolar plane image (EPI) is generated. The EPI contains three-dimensional information about objects. After processing the EPI, we can obtain the information of depth variation about objects and street-parking vehicles are detected from the depth information.

Detection of vehicles in panoramic range image

It is important to assess street-parking vehicles causing traffic problems in urban areas. However, assessments are performed manually and at high cost. Developing a detection system of those vehicles is a top priority for reducing cost and avoiding human error. We propose a detection method using a laser-range finder and a line-scan camera. In the detection method, two kinds of cluster analyses are applied to laser-range points: one is for clustering laser-range points at each scan, and the other for clustering laser-range points over several scans. Each cluster of laser-range points indicates a vehicle. As a result of verification experiments in real roads, a detection rate reached 90%.

Extraction of vehicle image from panoramic street-image

It is important to assess street-parking vehicles causing traffic problems in urban cities, however, it is performed manually and at a high cost. It is a top priority for reducing costs, to develop a detection system of those vehicles. We introduce a panoramic street-image, combined with view- and range- images. Panoramic street-image possibly provides useful information for our daily life. We propose a detection method, using a laser-range finder and a line-scan camera. Two kinds of cluster analysis are applied to range points: one is for clustering points at each scan, and the other for clustering points over several scans, each cluster of range points meaning a vehicle. As a result of verification experiments in real roads, a detection rate of 90 % is reached. Based on the results of clustering range data, all vehicle images are extracted from the corresponding panoramic street view-image. The error in extraction was within two scans of the laser-range finder.