Lighting for drivers

We worked with Stephan Voelker and Jan Winter of Berlin Technical University on issues associated with lighting for drivers.

In this first paper eye tracking was used to record drivers gaze behaviour. These data were used to establish the dominant location of fixations, as a step towards characterising a suitable background when estimating adaptation. On the main road eye movement was clustered within a circle of approximately 10° diameter, centred at the horizon of the lane: on the residential street eye movements were clustered slightly towards the near side, possibly a tendency to look toward the area of expected hazards.

Winter J, Fotios S, Völker S. Gaze direction when driving after dark on main and residential roads: Where is the dominant location? Lighting Research and Technology 2017; 49(5): 574-585.

This paper describes an experiment conducted to investigate how contrast threshold for target detection is affected by the presence of glare and by extraneous light sources. Glare equations tend to be based on tests using uniform, homogenous fields: the results of this experiment indicate that, in the presence of extraneous light sources, the influence of glare is over-estimated.

Winter J, Fotios S, Völker S. The effects of glare and inhomogeneous visual fields on contrast detection in the context of driving. Lighting Research and Technology 2018; 50(4): 537-551.

To estimate adaptation luminance we need to know the background luminance and typically this assumes a static field. That is a simplification because vehicle movement, eye movement, and environmental complexity mean the background is not static. The effect of dynamic fixation was estimated using eye tracking data. Background luminances were significantly higher when estimated using the dynamic assumption.

Winter J, Fotios S, Völker S. The effect of assuming static or dynamic gaze behaviour on the estimated background luminance of drivers. Lighting Research and Technology 2019; 51(3): 384-401.

Two experiments were carried out in a research project funded by Highways England.

One experiment examined driving in fog. A scale model apparatus was built that enabled fog to be simulated in a scene of the drivers view of a 3-lane carriageway. Luminance and SPD were varied under three fog conditions – none, thin and thick – and target detection performance was measured.

Fotios S, Cheal C, Fox S, Uttley J. The effect of fog on detection of driving hazards after dark. Lighting Research and Technology 2018; 50(7): 1024-1044.

The second experiment examined the effect of abrupt changes in road lighting. Target detection was compared before and after the road lighting was suddenly switched on or off.

Fotios S, Cheal C, Fox S, Uttley J. The transition between lit and unlit sections of road and detection of driving hazards after dark. Lighting Research and Technology 2019; 51(2): 243-261.

This paper presents a review of road lighting research and was included in the special issue to mark the 50th volume of Lighting Research and Technology. The article focuses on the basis of quantitative recommendations given in guidance documents, primarily how much light should be given. A key conclusions was that “Recommendations for the amount of light do not appear to be well-founded in robust empirical evidence, or at least do not tend to reveal the nature of any evidence.” If the basis of current guidance is unknown, then it is difficult to establish if the intended aims of road lighting are being met when the guidance is followed.

Fotios S, Gibbons R. Road lighting research for drivers and pedestrians: The basis of luminance and illuminance recommendations. Lighting Research and Technology 2018, 50(1): 154-186.

What sort of distraction is most likely to lead to a road traffic collision when driving? The media and many research papers focus on the risk of using a mobile phone when driving. We conducted a review of evidence. First, we examined papers in which drivers had been interviewed, often at hospital, following a crash. Give that drivers may not want to reveal their use of a mobile phone, we next examined papers reported field observation of the distractions drivers engage in. The results of both methods were in agreement: conversation with passengers and listening to music are the most prevalent types of distraction from driving.

Robbins CJ, Fotios S. Road lighting and distraction whilst driving: establishing the significant types of distraction. Lighting Research and Technology. Online First 8th April 2020.

The study of driver distraction was extended to consider the influence of road type. The results suggest an interaction: on minor roads the primary distraction is conversation with passengers, while on main roads such as motorways, where there tend to be fewer passengers, the primary distraction is mobile phone use.

Robbins CJ, Fotios S. The prevalence of in-vehicle driving distractions in road traffic collisions as a function of road type. Transportation Research Part F: Psychology and Behaviour 2022; 84: 211-222.

Having identified distractions to driving the next step was to determine how they affect hazard detection. This first experiment used an abstract situation - detection of targets on a screen. Distraction was imposed using either a control or a series of standardised tasks including an n-back task. The experiment also explored observer age and simple vs choice responses.

Fotios S, Robbins CJ, Fox SR, Cheal C, Rowe R. The effect of distraction, response mode and age on peripheral target detection to inform studies of lighting for driving. Lighting Research and Technology 2021; 53(7): 637-656