Mark Vollrath | Technische Universität Braunschweig (original) (raw)

Papers by Mark Vollrath

Accident Analysis and Prevention, 2010

Accident Analysis and Prevention, 2011

Accident Analysis and Prevention, 2009

Transportation Research Part F-traffic Psychology and Behaviour, 2011

Multiple studies have shown an increased accident risk due to telephoning while driving. On the o... more Multiple studies have shown an increased accident risk due to telephoning while driving. On the other hand, driving with passengers leads to a decreased accident risk. One explanation is a conversation modulation by passengers in cars which leads to a different conversation pattern which is not so detrimental to driving as that when phoning. A driving simulator study was conducted

Transportation Research Part F-traffic Psychology and Behaviour, 2011

ABSTRACT Distraction is a common cause of accidents (e.g. NHTSA, 2009), and different distraction... more ABSTRACT Distraction is a common cause of accidents (e.g. NHTSA, 2009), and different distraction conditions influence the driving behaviour in a specific way. Despite a lot of research in this area, most studies concentrate on perception errors as a result of visual distraction. The effects of different distraction conditions on higher cognitive processes are still unclear. The fact that accidents happen even if the drivers perceive all relevant information implies that cognitive factors contribute to accidents, too. For this reason, this study was conducted to investigate how different distraction conditions influence the anticipation of events in a car–following scenario. Anticipation is required to know what will happen next, and to react adequately to the situation. In a driving simulator, scenarios with different manoeuvres of a preceding car were created to generate various anticipations and therefore a different adaptation of the driving behaviour. Additionally, a cognitive and a visual secondary task were introduced. The question was in which way either a cognitive or visual distraction influences the generation of anticipations and the construction of an appropriate situation model.The results indicate that in the phase when the preceding car showed braking manoeuvres, drivers prolonged their safety distance only when being visually distracted which is probably done to compensate for this visual distraction. This compensation ensued to some extent in the second phase where the preceding cars drove with a constant speed. Additionally, drivers who were visually distracted went somewhat slower when the car in front had braked in phase one. Thus, the drivers seemed to anticipate that the car might brake again and adapted their speed accordingly. This was not found in drivers with cognitive distraction. Thus, cognitive distraction seems to disturb this anticipation.Finally, at an intersection, drivers with visual distraction had a smaller TTC and a higher velocity when the car in front made an unexpected turn. Thus, the impairment of perception due to the visual distraction leads to a slower reaction as compared to a cognitive distraction. Overall, cognitive distraction influences the anticipation of possible future actions of other car drivers negatively while visual distraction deteriorates perception and thus the reaction to critical, sudden events. Thus, different intervention strategies are required to prevent these kinds of accidents.

Accident Analysis and Prevention, 2010

Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has... more Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has the potential to support designers of new (partially autonomous) driver assistance systems (PADAS) in early stages with regard to understanding how assistance systems affect human driving behaviour. This paper presents the current research on an integrated driver model under development at OFFIS within the EU project ISi-PADAS. We will briefly show how we integrate improvements into CASCaS, a cognitive architecture used as framework for the different partial models which form the integrated driver model. Current research on the driver model concentrates on two aspects of longitudinal control (behaviour a signalized intersections and allocation of visual attention during car following). Each aspect is covered by a dedicated experimental scenario. We show how experimental results guide the modelling process.

Accident Analysis and Prevention, 2010

Accident Analysis and Prevention, 2011

Accident Analysis and Prevention, 2009

Transportation Research Part F-traffic Psychology and Behaviour, 2011

Multiple studies have shown an increased accident risk due to telephoning while driving. On the o... more Multiple studies have shown an increased accident risk due to telephoning while driving. On the other hand, driving with passengers leads to a decreased accident risk. One explanation is a conversation modulation by passengers in cars which leads to a different conversation pattern which is not so detrimental to driving as that when phoning. A driving simulator study was conducted

Transportation Research Part F-traffic Psychology and Behaviour, 2011

ABSTRACT Distraction is a common cause of accidents (e.g. NHTSA, 2009), and different distraction... more ABSTRACT Distraction is a common cause of accidents (e.g. NHTSA, 2009), and different distraction conditions influence the driving behaviour in a specific way. Despite a lot of research in this area, most studies concentrate on perception errors as a result of visual distraction. The effects of different distraction conditions on higher cognitive processes are still unclear. The fact that accidents happen even if the drivers perceive all relevant information implies that cognitive factors contribute to accidents, too. For this reason, this study was conducted to investigate how different distraction conditions influence the anticipation of events in a car–following scenario. Anticipation is required to know what will happen next, and to react adequately to the situation. In a driving simulator, scenarios with different manoeuvres of a preceding car were created to generate various anticipations and therefore a different adaptation of the driving behaviour. Additionally, a cognitive and a visual secondary task were introduced. The question was in which way either a cognitive or visual distraction influences the generation of anticipations and the construction of an appropriate situation model.The results indicate that in the phase when the preceding car showed braking manoeuvres, drivers prolonged their safety distance only when being visually distracted which is probably done to compensate for this visual distraction. This compensation ensued to some extent in the second phase where the preceding cars drove with a constant speed. Additionally, drivers who were visually distracted went somewhat slower when the car in front had braked in phase one. Thus, the drivers seemed to anticipate that the car might brake again and adapted their speed accordingly. This was not found in drivers with cognitive distraction. Thus, cognitive distraction seems to disturb this anticipation.Finally, at an intersection, drivers with visual distraction had a smaller TTC and a higher velocity when the car in front made an unexpected turn. Thus, the impairment of perception due to the visual distraction leads to a slower reaction as compared to a cognitive distraction. Overall, cognitive distraction influences the anticipation of possible future actions of other car drivers negatively while visual distraction deteriorates perception and thus the reaction to critical, sudden events. Thus, different intervention strategies are required to prevent these kinds of accidents.

Accident Analysis and Prevention, 2010

Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has... more Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has the potential to support designers of new (partially autonomous) driver assistance systems (PADAS) in early stages with regard to understanding how assistance systems affect human driving behaviour. This paper presents the current research on an integrated driver model under development at OFFIS within the EU project ISi-PADAS. We will briefly show how we integrate improvements into CASCaS, a cognitive architecture used as framework for the different partial models which form the integrated driver model. Current research on the driver model concentrates on two aspects of longitudinal control (behaviour a signalized intersections and allocation of visual attention during car following). Each aspect is covered by a dedicated experimental scenario. We show how experimental results guide the modelling process.