Evaluation of the effect of cooperative infrastructure-to-vehicle systems on driver behavior

Farah, Haneen; Koutsopoulos, Haris N.; Saifuzzaman, Mohammad; Kölbl, Robert; Fuchs, Susanne; Bankosegger, Doris

doi:10.1016/j.trc.2011.08.006

Cited by 73 publications

(26 citation statements)

References 13 publications

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“…A reckless driver, who drives fast and applies sudden breaks, will have a larger acceleration noise than the one who drives smoothly (Jones and Potts, 1962). Farah et al (2012) used acceleration noise to see if drivers' acceleration behaviours have improved when driving with cooperative systems. Belz and Aultman-Hall (2011) found that young drivers (i.e.…”

Section: Discussionmentioning

confidence: 99%

Impact of mobile phone use on car-following behaviour of young drivers

Saifuzzaman¹,

Boyle²,

Zheng³

et al. 2015

Accident Analysis & Prevention

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121

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Multitasking, such as the concurrent use of a mobile phone and operating a motor vehicle, is a significant distraction that impairs driving performance and is becoming a leading cause of motor vehicle crashes. This study investigates the impact of mobile phone conversations on car-following behaviour. The CARRS-Q Advanced Driving Simulator was used to test a group of young Australian drivers aged 18-26 years on a car-following task in three randomised phone conditions: baseline (no phone conversation), hands-free and handheld. Repeated measure ANOVA was applied to examine the effect of mobile phone distraction on selected car-following variables such as driving speed, spacing, and time headway. Overall, drivers tended to select slower driving speeds, larger vehicle spacings, and longer time headways when they were engaged in either hands-free or handheld phone conversations, suggesting possible risk compensatory behaviour. In addition, phone conversations while driving influenced car-following behaviour such that variability was increased in driving speeds, vehicle spacings, and acceleration and decelerations. To further investigate car-following behaviour of distracted drivers, driver time headways were modelled using Generalized Estimation Equation (GEE). After controlling for various exogenous factors, the model predicts an increase of 0.33s in time headway when a driver is engaged in hands-free phone conversation and a 0.75s increase for handheld phone conversation. The findings will improve the collective understanding of distraction on driving performance, in particular car following behaviour which is most critical in the determination of rear-end crashes.

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Section: Discussionmentioning

confidence: 99%

Impact of mobile phone use on car-following behaviour of young drivers

Saifuzzaman¹,

Boyle²,

Zheng³

et al. 2015

Accident Analysis & Prevention

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121

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“…These tend to fall in the same frequency range as the QRS-complexes, which can make it difficult to filter them without deforming the QRS complex (Kirst, Glauner, & Ottenbacher, 2011). In traffic related studies, ECG recordings have been used in for example (Brouwer & Dijksterhuis, 2015;Fallahi, Motamedzade, Heidarimoghadam, Soltanian, & Miyake, 2016;Farah et al, 2012;Miyaji, Danno, Kawanaka, & Oguri, 2008). The main disadvantage is that ECG is not easily measured unintrusively.…”

Section: Measuring Heart Rate In Naturalistic or Simulated Settingsmentioning

confidence: 99%

HeartPy: A novel heart rate algorithm for the analysis of noisy signals

Gent

Farah

Nes

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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146

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a b s t r a c tHeart rate data are often collected in human factors studies, including those into vehicle automation. Advances in open hardware platforms and off-the-shelf photoplethysmogram (PPG) sensors allow the non-intrusive collection of heart rate data at very low cost. However, the signal is not trivial to analyse, since the morphology of PPG waveforms differs from electrocardiogram (ECG) waveforms and shows different noise patterns. Few validated open source available algorithms exist that handle PPG data well, as most of these algorithms are specifically designed for ECG data.In this paper we present the validation of a novel algorithm named HeartPy, useful for the analysis of heart rate data collected in noisy settings, such as when driving a car or when in a simulator. We benchmark the performance on two types of datasets and show that the developed algorithm performs well. Further research steps are discussed.

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“…Side-sweep accidents lead to a side collision, but the resulting loss of control can create further collisions [12,13]. Side-sweep accidents account for 4%-10% of all crashes [14][15][16][17].…”

Section: The Physical and Cognitive Context Of A Lane Changementioning

confidence: 99%

Reducing Side-Sweep Accidents with Vehicle-to-Vehicle Communication

Bulumulle

Bölöni

2016

JSAN

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Side-sweep accidents are one of the major causes of loss of life and property damage on highways. This type of accident is caused by a driver initiating a lane change while another vehicle is blocking the road in the target lane. In this article, we are trying to quantify the degree to which different implementations of vehicle-to-vehicle (V2V) communication could reduce the occurrence of such accidents. We present the design of a simulator that takes into account common sources of lack of driver awareness such as blind-spots and lack of attention. Then, we study the impact of both traditional, non-technological communication means such as turning signals as well as unidirectional and bidirectional V2V communications.

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Evaluation of the effect of cooperative infrastructure-to-vehicle systems on driver behavior

Cited by 73 publications

References 13 publications

Impact of mobile phone use on car-following behaviour of young drivers

Impact of mobile phone use on car-following behaviour of young drivers

HeartPy: A novel heart rate algorithm for the analysis of noisy signals

Reducing Side-Sweep Accidents with Vehicle-to-Vehicle Communication

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