Roland Bremond

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.

ABSTRACT Cet article aborde les relations entre l&amp;#39;aménagement routier et la sécurité routière en France. La façon de concevoir l&amp;#39;éclairage routier est présentée et discutée. Plus particulièrement, la discussion de la caractérisation des installations d&amp;#39;éclairage par le modèle Visibilité de Petite Cible « Small Target Visibility » permet de montrer les limites d&amp;#39;un modèle qui ne prend pas en compte de l&amp;#39;activité de conduite dans les modèles de conception. Nous suggérons que la prise d&amp;#39;information visuelle et l&amp;#39;anticipation du conducteur doivent être prises en compte pour améliorer la conception de cet objet technique. Enfin, nous présentons un exemple de démarche que nous avons mis en oeuvre pour mettre en évidence les limites du modèle standard d&amp;#39;inspiration psychophysique.

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.

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.

The aim of this work is to assess how adding a driving-related task affects the detection of objects in peripheral vision, under mesopic conditions. The main index used to assess the quality of road lighting installations refers to simple detection tasks in foveal vision, which raises methodological and practical questions. The experimental design consisted of a three-phase experiment. In the first phase, two groups (control and experimental) performed a peripheral detection task (simple task). Based on these results an individual detection threshold was computed for each participant and eccentricity. A tracking task was performed in Phase 2 for both groups (steering a tracking target along a circuit, on a screen). In the third phase, the control group performed the same task as in Phase 2. The experimental group performed a double task, with a tracking (primary) task and a peripheral detection (secondary) task. The data show an effect of the tracking task and eccentricity on peripheral event detection. The tracking task caused detection performance to decrease from 84.2% to 67.5%, p &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt; .001. The small target visibility model used in road lighting may be improved, taking into account the effects of task and eccentricity on target detection. This study supports improved roadway lighting design by guiding consideration of sign eccentricity and task load.

ABSTRACT Modeling scenarios on driving simulators is a critical and complex task for behavioral researchers. It requires specific technical and programming skills, for which researchers are typically not formally trained. The main reason for this complexity is the lack of User-Centred Design (UCD) in scenario authoring tools, including an unintuitive and user-unfriendly interaction environment. This could account for some of the challenges faced by behavioral researchers in achieving their objectives with driving simulators. In this chapter, we discuss the problem in detail and propose a user-centred solution, which has been evaluated by users. We also propose a meta-language and an interoperability framework to generalize our solution, so that our approach could be used on most driving simulators.