LEPSIS

The Small Target Visibility (STV) model is the main model used to assess the quality of road lighting installations (IESNA, 2000). However, this model is based on a simple detection task in foveal vision using psychophysical data from laboratory conditions. The purpose of this study was to evaluate the impact of a complex background and apparent motion on target detection performance in mesopic vision, for three luminance contrasts, with reference to the STV scenario. To do so, participants were invited to detect standard square targets varying in terms of contrast presented in three Conditions: a uniform background, still images, and a video. Luminance levels were chosen in the mesopic domain relevant for road lighting at night. Images and video were chosen in relation to a driving task at night. The results showed that both the spatial context and the apparent motion had a negative impact on peripheral target detection performance: contrasts which are easy to detect in conditions close to the STV reference data may lead to poor performance if one adds context variables. These results give evidence that the STV model used for road lighting design based on laboratory data is limited, which strengthens previous results (Mayeur et al., 2008). The results are discussed in relation to the field factor used by practitioners to compensate for the differences between the STV reference scenario (detection of a small square target on a lit road while driving) and the STV psychophysical reference data.

The Small Target Visibility model used in road lighting design is based on a strong simplification of the driving task. To specify the driver’s visibility needs in a way consistent with state-of-the-art lighting engineering practice, a field experiment was designed in order to investigate target detection performances, comparing driver and passenger status conditions. Sixteen target visibility levels (VL) were used. In the driver status, 34 participants had to press a button as soon as they detected the target stimulus placed on the experimental lighted section. In the passenger status, the same participants had to detect the target stimulus while the experimenter drove. The results show that the passengers’ performances (detection distance) were higher than the drivers’ performances (p = .0014). Furthermore, the higher the VL, the higher the detection distance (p < .0001). These results lead up to modify the reference scenario in order to take into account human factor components for road lighting design.

Ifsttar is working with the scientific network of the French Department of Transportation to foster innovative camera-based solutions for assessing and improving highway visibility. This paper proposes an overview of the computer vision methods which result from this work. It encompasses camera-based advanced driver assistance systems (ADAS), camera-based traffic monitoring systems and camera-based road inspection systems, and tackles the question of validation.

Ifsttar is working with the scientific network of the French Department of Transportation to foster innovative camera-based solutions for assessing and improving highway visibility. This paper proposes an overview of the computer vision methods which result from this work. It encompasses camera-based advanced driver assistance systems (ADAS), camera-based traffic monitoring systems and camera-based road inspection systems, and tackles the question of validation.