Modelling Driver Behaviour in Automotive Environments

Cacciabue, Pietro Carlo

doi:10.1007/978-1-84628-618-6

Cited by 70 publications

(3 citation statements)

References 153 publications

(209 reference statements)

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“…Here, since the vehicle is no longer controlled by a human, it is important that its "driving behaviour", and how it negotiates different road geometries, and traffic conditions, is considered pleasant, and rated positively by the user, such as ensuring it feels comfortable and safe (Elbanhawi et al, 2015;Summala, 2007). Other, more psychological, terms and concepts used in this context include ensuring the AV is considered reliable, and familiar, avoiding any sudden surprise behaviours, which are shown to enhance acceptance, satisfaction and perceived safety of AVs (Carsten & Martens, 2018;Ramm et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural?

Peng¹,

Merat²,

Romano³

et al. 2021

Preprint

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Objective: This study investigated users’ subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. Background: Comfort and naturalness are thought to play an important role in contributing to users’ acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. Method: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking (SS) questionnaire, which assessed their risk-taking propensity. Results: Participants regarded human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. However, between the two human-like controllers, only the Defensive style was considered comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. Conclusion: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. Application: Knowing how different driver groups evaluate automated vehicle controllers is important to design more acceptable systems in the future.

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Section: Introductionmentioning

confidence: 99%

Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural?

Peng¹,

Merat²,

Romano³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Accordingly, drivers adopt a higher speed or a shorter headway when the driving task becomes easier, less risky, or less temporally demanding due to a change in the road-vehicle-driver system, such as improved environmental conditions (e.g., increased speed that occurs when adding road lighting; Assum et al, 1999) or increased controllability of the car itself (e.g., increased speed and shorter headway time adopted when driving with an adaptive cruise control; Dragutinovic et al, 2005). The magnitude of the BA effects is thought to depend on the time driven with the ADAS, the driver's attitude towards the ADAS (e.g., whether the driver uses the system to drive to the limit), driver experience, and the design of ADAS (Carsten, Lai, Barnard, Jamson, & Merat, 2012;Saad, Hjälmdahl, Cañas, & Alonso, 2004;Sullivan, Flannagan, Pradhan, & Bao, 2016). One supposedly important predictor of BA is the ADAS' noticeability: It has been said that ADAS which cause directly noticeable differences in the road-vehicle-driver system suffer from BA to a greater extent than ADAS that do not (Elvik, Vaa, Hoye, & Sorensen, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Prior to each guidance condition, drivers were familiarized with the working mechanisms of the steering guidance. This was done because a BA effect may not appear immediately but rather appears after a familiarization period that allows drivers to update their mental model of the system (Beggiato, Pereira, Petzoldt, & Krems, 2015;Bianchi Piccinini, Rodrigues, Leitao, & Simoes, 2014;Martens & Jenssen, 2012;Saad, 2004;Sullivan et al, 2016). To enhance the familiarization process, each guidance condition was explained to the participants in detail.…”

Section: Introductionmentioning

confidence: 99%

Does haptic steering guidance instigate speeding? A driving simulator study into causes and remedies

Melman

Winter

Abbink

2017

Accident Analysis & Prevention

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C o v e r i ma g e a d o p t e d f r o m: F o t o k e r e n ( 2 0 1 5 , No v e mb e r ) . Ni g h t h i g h s p e e d c a r f u n [ Di g i t a l i ma g e ] . Re t r i e v e dMa y 2 3 , 2 0 1 6 , f r o m h t t p : / / f o t o k e r e n . n e t / n i g h t -h i g h -s p e e d -c a r -f u n / Driving a car has become a major part of our life. To assist drivers during this task new Advanced Driver Assistance Systems (ADAS) are developed to increase safety and comfort. In reality, however, these safety benefits are often diminished because of adaptations by the driver, such as adopting a higher speed or driving closer to the vehicle in front. In current literature, I found many theories trying to explain why people adapt, but few studies investigating ways to prevent these negative adaptations from occurring (entire literature study is available at http://repository.tudelft.nl/ for more detail). In this MSc. thesis I took the first step from explaining towards preventing behavioral adaptation, with the aim to make novel ADAS more effective in terms of safety.This thesis report is part of the fulfillment for the Master degree of Biomechanical Design at the Delft University of Technology. The driving simulator code, Matlab code used for the data analysis, and statistical analyses have been submitted to the BioMechanical Engineering depository on a USB stick, which is available on request. T. Melman Delft, May 2016iii Acknowledgments First, I would like to thank both my supervisors David Abbink and Joost de Winter for their enthusiasm and support during my MSc. thesis. David always knew how to motivate me, and was there to oversee the bigger picture, whereas Joost, with his keen eye for details, assisted me with tremendous devotion, especially during the last couple of weeks in order to send my thesis to a journal before my graduation. Although we had many memorable discussions, one moment in particular deserves to be mentioned in this acknowledgment; at Sunday evening prior to my experiment, I had some serious concerns. That very same evening, both helped me out, and took these concerns away. Despite the late hour, both were available to help me when I needed them the most. Abstract-An important issue in road traffic safety is that drivers show adverse behavioral adaptation (BA) to driver assistance systems. Haptic steering guidance is an upcoming assistance system which facilitates lane-keeping performance while keeping drivers in the loop, and which may be particularly prone to BA. Thus far, most experiments on haptic steering guidance have measured driver performance while the vehicle speed was kept constant, and so the degree of BA could not be established. The aim of the present driving simulator study was to examine whether haptic steering guidance causes BA in the form of speeding, and to evaluate two types of haptic steering guidance designed not to suffer from BA. Twenty-four participants drove a 1.8 m wide car for 13.9 km on a curved road, with cones demarcating a single 2.2 m narrow lane. Participants comple...

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Vehicular Mobility Modeling for VANET

Härri¹

2009

Vanet

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Modelling Driver Behaviour in Automotive Environments

Cited by 70 publications

References 153 publications

Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural?

Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural?

Does haptic steering guidance instigate speeding? A driving simulator study into causes and remedies

Vehicular Mobility Modeling for VANET

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