Urban Perception -- A Cross-Correlation Approach to Quantify the Social Interaction in a Multiple Simulator Setting

Lehsing, Christian; Kracke, Andrea; Bengler, Klaus

doi:10.1109/itsc.2015.169

Cited by 12 publications

(10 citation statements)

References 6 publications

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“…We investigated the effectiveness of an in-vehicle display that provided real-time information about the approach speed and direction of another road user at intersections. In this research, two driving simulators were connected so that the participant encountered a human experimenter in the same virtual world, an approach that is relatively rare but gaining popularity in human factors research (Hancock and De Ridder, 2003;Lehsing et al, 2015;Muehlbacher et al, 2014;Oeltze and Schiebl, 2015;Preuk et al, 2016). This linked-simulator method allowed for realistic behaviours of the road users, while retaining adequate control over the intersection situations.…”

Section: Discussionmentioning

confidence: 99%

Concurrent audio-visual feedback for supporting drivers at intersections: A study using two linked driving simulators

et al. 2017

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A large portion of road traffic crashes occur at intersections for the reason that drivers lack necessary visual information. This research examined the effects of an audio-visual display that provides real-time sonification and visualization of the speed and direction of another car approaching the crossroads on an intersecting road. The location of red blinking lights (left vs. right on the speedometer) and the lateral input direction of beeps (left vs. right ear in headphones) corresponded to the direction from where the other car approached, and the blink and beep rates were a function of the approaching car's speed. Two driving simulators were linked so that the participant and the experimenter drove in the same virtual world. Participants (N = 25) completed four sessions (two with the audio-visual display on, two with the audio-visual display off), each session consisting of 22 intersections at which the experimenter approached from the left or right and either maintained speed or slowed down. Compared to driving with the display off, the audio-visual display resulted in enhanced traffic efficiency (i.e., greater mean speed, less coasting) while not compromising safety (i.e., the time gap between the two vehicles was equivalent). A post-experiment questionnaire showed that the beeps were regarded as more useful than the lights. It is argued that the audio-visual display is a promising means of supporting drivers until fully automated driving is technically feasible.

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Section: Discussionmentioning

confidence: 99%

Concurrent audio-visual feedback for supporting drivers at intersections: A study using two linked driving simulators

et al. 2017

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“…There were two types of pedestrians: preprogrammed, computer controlled pedestrians (bot), similar to those used in prior driving simulator studies to evaluate the effects of vision impairment on pedestrian hazard detection (Alberti, Horowitz et al, 2014;Alberti, Peli et al, 2014;Bronstad et al, 2013;Bronstad et al, 2015) and intelligent, human-controlled pedestrians (ped) (Lehsing et al, 2015;Lehsing et al, 2016), as described above. The bot was controlled by a preprogrammed script which triggered it to start moving when the participant's vehicle was 46 meters from the predefined crossing point (see Appendix A).…”

Section: Driving Scenarios Pedestrianmentioning

confidence: 99%

“…This method has successfully been used in different combinations in prior studies investigating mutual behavior adaption processes (i.e. social interaction) among different types of normally-sighted road users for example pedestrians, motorcycles and vehicles, (Hancock & de Ridder, 2003;Lehsing, Benz, & Bengler, 2016;Lehsing, Kracke, & Bengler, 2015;Mühlbacher, Preuk, Lehsing, Will, & Dotzauer, 2017;. The behavior adaption between two individuals takes time; hence, a time series analysis approach was used.…”

Section: Introductionmentioning

confidence: 99%

“…The behavior adaption between two individuals takes time; hence, a time series analysis approach was used. This allowed a quantification of the degree of mutual adaption, how long the adaptation took and which road user was dominating the adaption process (Lehsing et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Our primary hypothesis was that the mild vision loss would delay the initial stage of an interaction (i.e., the first glance toward the pedestrian), which might in turn delay subsequent interactions and behavior modifications such as braking. In addition we evaluated how behaviors differed in interactions with the human-controlled pedestrians (Lehsing et al, 2015;Lehsing & Feldstein, 2017) compared to preprogrammed computer-controlled pedestrians (bots), similar to those used in prior studies of drivers with real vision loss (Alberti, Horowitz et al, 2014;Bronstad et al, 2013). We hypothesized that mutual adaptation would take longer for the human-human than the human-bot interactions and that the mild vision loss might exacerbate differences in mutual adaptation between humanhuman and human-bot situations.…”

Section: Introductionmentioning

confidence: 99%

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Effects of simulated mild vision loss on gaze, driving and interaction behaviors in pedestrian crossing situations

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Accident Analysis & Prevention

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Purpose.-Interaction is the process of behavior adaption between two or more participants primarily based on what they visually perceive. It is an important aspect of traffic participation and supports a safe and efficient flow of traffic. However, prior driving simulator studies investigating the effects of vision impairment have typically used pre-programmed pedestrians that did not interact with the human driver. In the current study we used a linked pedestrian and driving simulator setting to increase the ecological validity of the experimental paradigm. We evaluated the effects of mild vision loss on interactions between drivers and human-controlled, interactive pedestrians compared to preprogrammed, non-interactive pedestrians.

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Measuring and Describing Cooperation Between Road Users—Results from CoMove

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Safe and efficient traffic requires that road users interact and cooperate with each other. Especially in situations which are not explicitly regulated, and the right of way is not clearly defined, it is of great importance that road users are able to communicate their own intentions and understand the communication and cooperation behaviour of the other involved road users. When automated vehicles enter the current traffic system, their ability to fit into the system, that is their ability to communicate and cooperate, will determine their success. Therefore, the development of cooperatively interacting, automated vehicles requires detailed knowledge about human cooperation behaviour in traffic, which can only be obtained using appropriate methods and measures. By focusing on road narrowings and lane changing, this chapter gives an overview on how to measure cooperation between road users, considering methods for data collection, subjective and objective measures of cooperation as well as behaviour modeling, to support the systematic research on cooperation in road traffic. This overview is extended by findings from studies conducted within CoInCar, including results on factors influencing human behaviour in cooperative situations, either in a manual or an automated setting, and initial findings from modeling the cognitive processes underlying cooperative driving behaviour.

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Urban Perception -- A Cross-Correlation Approach to Quantify the Social Interaction in a Multiple Simulator Setting

Cited by 12 publications

References 6 publications

Concurrent audio-visual feedback for supporting drivers at intersections: A study using two linked driving simulators

Concurrent audio-visual feedback for supporting drivers at intersections: A study using two linked driving simulators

Effects of simulated mild vision loss on gaze, driving and interaction behaviors in pedestrian crossing situations

Measuring and Describing Cooperation Between Road Users—Results from CoMove

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