%0 Journal Article
%T Lidar Data Analysis for Time to Headway Determination in the DriveSafe Project Field Tests
%A £¿lker Altay
%A Bilin Aksun G¨¹ven£¿
%A Levent G¨¹ven£¿
%J International Journal of Vehicular Technology
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/749896
%X The DriveSafe project was carried out by a consortium of university research centers and automotive OEMs in Turkey to reduce accidents caused by driver behavior. A huge amount of driving data was collected from 108 drivers who drove the instrumented DriveSafe vehicle in the same route of 25£¿km of urban and highway traffic in Istanbul. One of the sensors used in the DriveSafe vehicle was a forward-looking LIDAR. The data from the LIDAR is used here to determine and record the headway time characteristics of different drivers. This paper concentrates on the analysis of LIDAR data from the DriveSafe vehicle. A simple algorithm that only looks at the forward direction along a straight line is used first. Headway times based on this simple approach are presented for an example driver. A more accurate detection and tracking algorithm taken from the literature are presented later in the paper. Grid-based and point distance-based methods are presented first. Then, a detection and tracking algorithm based on the Kalman filter is presented. The results are demonstrated using experimental data. 1. Introduction (National Highway Traffic Safety Administration) NTHSA reported six million vehicle crashes in 2005 that resulted in 43,000 dead and 2.5 million injured people in the USA [1]. Moreover, (International Road Traffic and Accident Database) IRTAD reported 40,000 dead and 1.4 million injured people all over Europe in the same year. Driver mistakes caused 90 percent of these crashes [2]. Driving dynamics should be investigated to prevent accidents caused by driver mistakes before they take place. Driving dynamics is the interaction between vehicle, road conditions, and the driver [3]. The theoretical model of driving behaviour based on a microsimulator is presented in [4] where the driver error is separated into perception, decision-making, and action parts. According to [5], the development in passive and active safety systems, car accidents, and fatalities per distance decrease as the total distance travelled per year increases. The DriveSafe project was started to reduce accidents caused by driver behavior with the collaboration of OTAM, Sabanci University, £¿stanbul Technical University, Ford A.£¿., Renault A.£¿., and Tofas A.£¿. A huge amount of driving data was collected in 2006. A total of 108 drivers, 89 men and 19 women, drove the DriveSafe vehicle in the same route of 25£¿km in £¿stanbul [6, 7]. This instrumented vehicle, used for multimodal data collection, is a Renault Megane see (Figure 1) equipped with a large array of sensors and a data acquisition
%U http://www.hindawi.com/journals/ijvt/2013/749896/