Risky attitudes towards road use in pre-drivers

Motor vehicle accidents are one of the principal causes of adolescent disability or mortality and male drivers are more likely to be involved in road accidents than female drivers. In part such associations between driver age and sex have been linked to differences in risky behaviour (e.g. speed, violations) and individual characteristics (e.g. sensation seeking, deviant behaviour). The aim of this research is to determine whether associations between risky road user behaviour and individual characteristics are a function of driver behaviour or whether they are intrinsic and measurable in individuals too young to drive. Five hundred and sixty-seven pre-driver students aged 11-16 from three secondary schools completed questionnaires measuring enthusiasm for speed, sensation seeking, deviant behaviour and attitudes towards driver violations. Boys reported more risky attitudes than girls for all measures. Associations between sensation seeking, deviant behaviour and attitudes towards risky road use were present from early adolescence and were strongest around age 14, before individuals learn to drive. Risky attitudes towards road use are associated with individual characteristics and are observed in adolescents long before they learn to drive. Safe attitudes towards road use and driver behaviour should be promoted from childhood in order to be effective. (C) 2007 Elsevier Ltd. All rights reserved.

Analysis of full-waveform LiDAR data for classification of an orange orchard scene

Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient c was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and c. For single-peak waveforms the scatterplot of c versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return c values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the c versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient c of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties.