The implications for steady-state experimental scenarios versus more dynamic ones are discussed.
PEDAL PROJECTS OWLY DRIVERS
In both experiments, we found no evidence that drivers adjust their safety margins to account for the additional demands of performing a cognitive task. In Experiment 2, we examined safety margins and performance under less constrained, yet more realistic and dynamic conditions. In Experiment 1, we explored the safety margins (distances) that drivers maintain between themselves and vehicles around them when completing a passing maneuver.
PEDAL PROJECTS OWLY SIMULATOR
In two simulator experiments, we examined the impact of mental workload on drivers engaged in a ?naturalistic? tactical driving task. intentions are constantly changing (i.e., involving tactical vehicle control). However, these studies often test steady-state behaviors (e.g., car-following) that might not be representative of traffic situations in which drivers' goals and. Previous work has shown that drivers engaged in concurrent cognitive tasks exhibit some adaptive behaviors to enhance safety, such as increasing their headway distance, despite the fact that other aspects of safety might be compromised. The presentation timing of vehicle navigation systems for each traffic condition is discussed based on the road structures of turning points.
In contrast, the onset of braking occurred further before the intersection when drivers had a forward vehicle while approaching intersections after curves, where drivers cannot readily determine the distance between their vehicle and the turning point until just before the intersection. The results suggest that braking began closer to the target intersection when driving with a vehicle in front, independent of the number of traffic lanes or the turning direction.
PEDAL PROJECTS OWLY DRIVER
Data were analysed for relations between driver preparations, different traffic conditions (with or without forward and/or following vehicles) and the intersection type and turning direction. Instrumented vehicles recorded drivers' preparatory behaviour, including moving the right foot to cover the brake pedal and activating the turn-signal, while approaching the turning point on public roads. This study analyses naturalistic driving behaviour before making turns at intersections. Road conditions are also influential on driving behavior prediction.
Experimental evaluations show that distractive conditions have a certain effect on driving behavior, where the prediction errors are significantly increasing in these conditions. The temporal clustering is performed with hidden Markov model (HMM). We propose a behavior prediction system, which performs temporal clustering of behavior signals and computes linear estimators for each temporal cluster. In this study, we investigate driving behavior prediction from past driving signals. Driving behavior is strongly related to past actions of drivers. Driver status identification over ten drivers with task and no-task classes yields a promising 79.13% task identification rate.
Driver identification rate within groups of three drivers is computed as 85.21%. Driver identification system with reduced number of drivers fits better on real-life scenarios. Driver identification over 23 drivers achieves a 57.39% identification rate with the fusion of gas and brake pedal pressure classifiers.
Experimental results over the UYANIK database are presented. Statistically significant clues of these investigations are used to define driver and driving status models. In this study, we investigate how driving behavior signals differ among drivers and among different driving tasks. Driving behavior signals differ in how and under which conditions the driver uses vehicle control units, such as pedals, driving wheel, etc.
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