Internal Distraction and Driving: Does It Show?

The effect of daydreaming (‘internal distraction’) on driving behavior little is known. Since it happens to some extent to most drivers, an explorative study was performed to see whether in an experimental setting something like daydreaming could occur, and if so whether this would show up in driving behavior. Three groups of participants made two drives in the TNO driving simulator. Group 1 did not perform any secondary task, Group 2 performed a ‘thinking and reasoning’ task (daydreaming condition) during specific parts of the drive, and Group 3 performed a ‘listening and remembering’ task during the same sections of the drives as Group 2. Mostly an effect was found for the ‘listening and remembering’ task. If an effect was found for the internal distraction condition, it indicated a same (negative) effect as the ‘listening and remembering’ task, although less severe

D41.1 : Performance Indicators and ecoDriver Test Design

This deliverable details the proposed assessment approaches and the design of field trials for data provision. Research questions and objectives of the project were divided into three major themes: user acceptance, behaviour, as well as energy use and emissions, which led to the formation of 24 hypotheses in total. A large number of Performance Indicators were identified, which will be used to validate the hypotheses. These Performance Indicators were grouped into 16 categories, covering the aforementioned three research themes. To provide empirical data for validating the hypotheses and answering the research questions, a series of field trials will betaken place in SP3. There are 12 fleets of vehicles, across 7 countries and covering a wide range of vehicle types. This deliverable outlines experimental design of the field trials, including fleet specifications, participant recruitment, route selection, test procedures, and data collection protocol etc. There are similarities but also individual characteristics of these experimental designs across the fleets and test sites, in order to produce all necessary data for addressing the research question

Linking behavioral indicators to safety: What is safe and what is not?

Safety is defined by the interactions and relationships between road-users, vehicles and the 21 infrastructure. But what is it that determines whether a situation is critical or unsafe? Due to 22 the many disadvantages of analyzing accident statistics, safety is often defined as its ‘output 23 measures’, or proximal or behavioral safety indicators. Examples are speed, speed variability, 24 time headway, SDLP, TLC and TTC. But where do we draw the line, what are good cut-off 25 values for these behavioral indicators? In order to come up with international standards, more 26 research is needed to fill in knowledge gaps. This article provides an overview of the link 27 between behavioral indicators and traffic safety. It also discusses earlier attempts and new 28 possibilities for setting cut-off values. The central research question in a new TNO project is 29 how to link behavioral indicators to what can be qualified as safe or unsafe. This paper is a 30 call to join forces and combine the existing data of naturalistic driving studies and field 31 experiments with new research. There is a need to combine behavioral indicators into one risk 32 factor, and add the link with behavior in specific surroundings. Cut-off values can be the end 33 result of a large research proposal, combining data from alcohol studies, visual distraction 34 data and driver drowsiness studies

Methodological challenges and solutions in the EuroFOT project

The euroFOT project is undertaking Field Operational Tests to investigate the effects of eight safety functions. More than 1500 drivers of cars and trucks will participate with the focus being not only on the use of the systems under daily traffic conditions but also their impact on traffic safety, efficiency and environment. In order to do this, a methodology had to be developed that balanced rigorous experimental methods with the practicalities of running a field trial. This paper describes how the methodology for undertaking comparative analysis between the functions was developed, drawing on the FESTA guidelines

Internal Distraction and Driving: Does It Show?

The effect of daydreaming (‘internal distraction’) on driving behavior little is known. Since it happens to some extent to most drivers, an explorative study was performed to see whether in an experimental setting something like daydreaming could occur, and if so whether this would show up in driving behavior. Three groups of participants made two drives in the TNO driving simulator. Group 1 did not perform any secondary task, Group 2 performed a ‘thinking and reasoning’ task (daydreaming condition) during specific parts of the drive, and Group 3 performed a ‘listening and remembering’ task during the same sections of the drives as Group 2. Mostly an effect was found for the ‘listening and remembering’ task. If an effect was found for the internal distraction condition, it indicated a same (negative) effect as the ‘listening and remembering’ task, although less severe

Drowsy drivers' under-performance in lateral control:how much is too much? Using an integrated measure of lateral control to quantify safe lateral driving

\u3cp\u3eInternationally, drowsy driving is associated with around 20% of all crashes. Despite the development of different detection methods, driver drowsiness remains a disconcerting public health issue. Detection methods can estimate drowsiness by directly measuring the physiology of the driver, or they can measure the effect that drowsiness has on the state of the vehicle due to the behavioural changes that drowsiness elicits in the driver. The latter has the benefit that it could measure the net effect that drowsiness has on driving performance which links to the actual safety risk. Fusing multiple sources of driving performance indicators like lane position and steering wheel metrics in order to detect drowsiness has recently gained increased attention. However, not much research has been conducted with regard to using integrated measures to detect increased drowsiness within an individual driver. Different levels of drowsiness are also rarely classified in terms of safe or unsafe. In the present study, we attempt to slowly induce drowsiness using a monotonous driving task in a simulator, and fuse lane position and steering wheel angle data into a single measure for lateral control performance. We argue that this measure is applicable in real-time detection systems, and quantitatively link it to different levels of drowsiness by validating it to two established drowsiness metrics (KSS and PERCLOS). Using level of drowsiness as a surrogate for safety we are then able to set simple criteria for safe and unsafe lateral control performance, based on individual driving behaviour.\u3c/p\u3