Vincent Ledoux

Photometric measurements for visibility level computations

Visibility level is a standard quality index in road lighting design. Three photometric values are needed to compute the visibility level of a target: its luminance, the luminance of its near background and the adaptation luminance. We discuss the consequences of how these parameters are set on the ability to predict the performance of a driver at target detection. An experiment was designed using a closed-road circuit. Sixteen targets were presented twice to 34 subjects. Six visibility level assessment methods were compared. The background luminance was set as the luminance at the bottom of the target, as the mean luminance around the target, or as the luminance associated with the maximum target contrast. The adaptation luminance was set either to the background luminance or to the mean luminance. The best non-linear fit between computed visibility level and target detection performance was found using the maximum contrast over the four sides of the square target, and setting the adaptation luminance to a unique estimated road luminance instead of the local background luminance. Even so, the variability of the visibility level data suggests great caution when using it as an index of road visibility.

Measuring the effect of the rainfall on the windshield in terms of visual performance

Driving through rain results in reduced visual performance, and car designers have proposed countermeasures in order to reduce the impact of rain on driving performance. In this paper, we propose a methodology dedicated to the quantitative estimation of the loss of visual performance due to the falling rain. We have considered the rain falling on the windshield as the main factor which reduces visual performance in driving. A laboratory experiment was conducted with 40 participants. The reduction of visual performance through rain was considered with respect to two driving tasks: the detection of an object on the road (contrast threshold) and reading a road sign. This experiment was conducted in a laboratory under controlled artificial rain. Two levels of rain intensity were compared, as well as two wiper conditions (new and worn), while the reference condition was without rain. The reference driving situation was night driving. Effects of both the rain level and the wipers characteristics were found, which validates the proposed methodology for the quantitative estimation of rain countermeasures in terms of visual performance.

Acquisition and analysis of road incidents based on vehicle dynamics

Because motor vehicle crashes have decreased during the last decade in many countries in the world and are more diffuse, local authorities have difficulties to define road safety policies. An experiment with 51 cars of public fleets equipped with a specific Event Data Recorder was carried out in France during one year. The purposes of this research were to evaluate if incident data (critical driving situations) help to understand crashes, and to explore a new way for road infrastructure safety diagnosis. The analysis of 339 genuine incidents and 1237 simple events recorded illustrates the potentiality of such an experiment and provides: some insights about conditions in which incidents occur, a general overview of their distribution according to different road layouts, as well as information on the different levels of accelerations reached. It can be noticed that there is an overrepresentation of incidents in right curves compared to left curves. The simple events involving mostly the infrastructure could be used to detect road defects. Genuine incidents where the vehicle is subjected to important dynamic demands, related to potentially unsafe driving situations, can be used to improve knowledge of the motor vehicle crashes thanks to incident mechanisms analysis.