Measures for behaving safely in traffic MeBeSafe

Daily driving as a relatively well-trained process is based on both, conscious and sub-conscious decision-making processes and auto-mated/reflective behaviours. These habituated behavioural patterns and habituation effects can lead to uninten-tional traffic violations due to late intervention. In these cases today's conventional traffic interventions are insufficient. The H2020 project MeBeSafe develops and tests innovative measures to increase safety margins according to the concept of nudging. The central approach is to stimulate the driver to show a desired choice without forbidding alternatives. As part of the project, dynamic lighting systems were installed in Eindhoven's infrastructure and examined in field tests for their suitability as speed reduction measures. Additionally, an in-vehicle nudging measure has been developed to alert the driver to potentially dangerous situations through an in-car human-machine-interface. In this paper, the application of such implementa-tions in infrastructure and vehicle development will be discussed on the one hand and potentials and obstacles associated with the implementation on a larger scale will be examined on the other hand.

After over 130 years of motorized road transportation, daily driving can be defined as a relatively well-trained process, relying on habitual patterns. These are based on a sound proportion of both, elaborate (sub-) conscious decision-making processes and automated/reflexive behaviors. However, apart from human factors, navigating safely through daily traffic inevitably needs additional guidance by road traffic regulations. On a macroscopic level, the driver´s acquaintance with such rules is precondition and needs to be verified by a valid driver´s license. However, accident statistics show that even in times of supportive ADAS and various traffic interventions like rumble strips, radar, intelligent traffic signs etc., a high number of severe road accidents appears to be still an issue on European roads. The authors follow the approach that conventional traffic interventions are insufficient when unintentional violations are concerned and hence, appear to be ineffective due to the following reasons: First, they do intervene too late in time. For instance, getting a fine due to speeding on a certain road section happens late after the misbehavior has occurred. Second, conventional traffic interventions lack effectivity due to habituation effects. In this vein, the present paper empirically surveys innovative measures following the concept of nudging in order to increase safety margins. This concept has been adopted from behavioral economics and relates to subconsciously stimulating humans to make a desired choice without prohibiting alternative choices. Nudging works on a subliminal level, is less invasive, provides humans with a choice (by predisposing a desired choice) and can be applied both, earlier as other measures as well as on demand in the chain of events leading to critical situations. The present paper targets driving at an inappropriate speed as of several causes for dangerous situations and accidents. Following the concept of nudging, the authors assume that dynamic infrastructure nudging solutions explicitly targeting drivers going at an inappropriate speed, have the potential to make these drivers adjust their speed to an appropriate level in a more effective way than conventional road signs do. Hence, the authors hypothesize that light-emitting nudging solutions have a stronger impact on drivers to reduce speed in a given situation than non-dynamic measures do. Second, they hypothesize that dynamic nudging solutions using moving lights towards the driver have a stronger impact to reduce speed in a given situation than non-moving lights do, because of the visual impression of the driving speed to be higher than it actually is. The empirical experiment consisted of three conditions. Condition 1 was a baseline showing conventional road signs indicating the desired speed level without any nudging measures. Condition 2 and 3 consisted of light emitting spots mounted onto the road. The spots were programmed either to be statically illuminated (condition 2) or to move dynamically towards the drivers in order to slow them down by creating a visual illusion of driving faster than they actually do (condition 3). In a driving simulator study, N = 54 participants drove through a simulated real-life motorway exit where dangerous driving behavior had been spotted before. The results show that participants reduce their speed significantly in the nudging-conditions in comparison to conventional measures. Furthermore, the hypothesis stating that moving lights toward the driver have a higher impact on speed reduction than non moving-lights can be confirmed as well. Further specification and testing is needed to determine the final design of such measures and their generalizability to other traffic situations. Furthermore, the influence of such new stimuli on the choice of an appropriate trajectory depending on the speed in a hazardous situation will have to be investigated.

Daily driving can be defined as a relatively well-trained process, relying on habitual patterns. These are based on both, conscious and subconscious decision-making processes and automated/reflective behaviours. Today's conventional traffic interventions are insufficient when unintentional violations are concerned due to a too-late intervention or habituation effects. The H2020-project MeBeSafe provides innovative measures to increase safety margins following the concept of nudging, which relates to stimulating humans to make a desired choice without prohibiting alternatives. Measures to reduce speeding through nudging via a dynamic light system in the infrastructure have now been installed for field testing in Eindhoven. Additionally, an in-vehicle nudging measure has been developed to direct the attention of the driver to potentially hazardous situations through an in-car human-machine-interface. This paper evaluates the application of these measures for future infrastructure and vehicle development and discusses the opportunities and inhibitions that come along with implementation on a larger scale.