Anticipatory skills are a potential factor for novice drivers' curve accidents. Behavioural data show that steering and speed regulation are affected by forward planning of the trajectory. When approaching a curve, the relevant visual information for online steering control and for planning is located at different eccentricities, creating a need to disengage the gaze from the guidance of steering to anticipatory look-ahead fixations over curves. With experience, peripheral vision can be increasingly used in the visual guidance of steering. This could leave experienced drivers more gaze time to invest on look-ahead fixations over curves, facilitating the trajectory planning. Eighteen drivers (nine novices, nine experienced) drove an instrumented vehicle on a rural road four times in both directions. Their eye movements were analyzed in six curves. The trajectory of the car was modelled and divided to approach, entry and exit phases. Experienced drivers spent less time on the road-ahead and more time on the look-ahead fixations over the curves. Look-ahead fixations were also more common in the approach than in the entry phase of the curve. The results suggest that with experience drivers allocate greater part of their visual attention to trajectory planning.

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We developed a method to quantify lead time and distance of look-ahead fixations in curve driving from on-road eye movement data. The results are relevant for driver modelling and development of anticipation training programmes for novice drivers.

Moving in natural environments is guided by looking where you are going. When entering a bend, car drivers direct their gaze toward the inside of the curve, in the region of the curve apex. This behavior has been analyzed in terms of both "tangent point models," which posit that drivers are looking at the tangent point (TP), and "future path models," which posit that drivers are visually targeting a point on the desired trajectory or future path (FP). This issue remains unresolved, partly due to the challenge of representing the changing visual projection of the trajectory into the driver's field of view. This paper reports a study of naturalistic driving, in which the FP in the field of view is explicitly modeled, and the TP and reference points on the FP are simultaneously analyzed as potential gaze targets. We argue that traditional area-of-interest methods commonly interpreted as supporting the TP hypothesis are problematic when the interest is contrasting multiple gaze targets. This prompts a critical reassessment of the empirical case for the ubiquity of looking at the TP and the generality of the TP hypothesis as an account of where people look when they steer. As a basis for representing driver gaze behavior, the FP is an equally valid point of departure. There are no overwhelming theoretical or empirical reasons for favoring the TP models over the FP models.

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Where do drivers look when approaching curves on a winding road? Existing models on visual processes in curve driving have focused on path-controlling behaviour. Another aspect in curve driving is the visual anticipation of potential oncoming vehicles, obstacles and road alignment. We define the occlusion point of a curve as the nearest point where the view of the road is blocked by some obstacle (e.g. vegetation). Monitoring the occlusion point is relevant for safe driving because potential oncoming vehicles or obstacles on the road will come into view on the occlusion point. In the current on-road study, ten participants drove an instrumented car at their own pace on a low standard rural road while their eye-movements were recorded. We investigated anticipatory glances towards the occlusion point while approaching open sight curves and how anticipatory glances are affected by a cognitive secondary task without explicit visuospatial or motor components. The results demonstrate that drivers indeed look at the occlusion point while approaching open curves on rural roads, and that working memory load leads to a significant decrease in visual anticipation. Previously, it has been shown that cognitive secondary tasks lead to reduction of looking at the speedometer and mirrors and of safety critical visual scanning at street crossings. We show that the effect is also present in the anticipation of road curvature and hazards on rural roads.

In order to implement the recommended Commission Internationale de l'Eclairage (CIE) system for mesopic photometry to roads, it is necessary to define the relevant visual field and adaptation luminance in night-time driving conditions. We measured three drivers' eye tracking on a rural road at night and in daytime, and the simultaneous luminance for the corresponding parts of the scene on lit and unlit sections of the road at night. Fields of view with circular sizes of 18, 58, 108, 158 and 208, with the centre point at the mode of the gaze distributions of the drivers, were used as initial estimates of the visual adaptation field. In both the lit and unlit sections, the variation within subject and between subjects in the mean luminance decreased as the size of the circular field increased. However, the mean luminances of all of the circular fields in the unlit section were higher than in the lit section due to the use of high-beam headlights in the unlit section.

Objective: This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.