Negative Behavioral Adaptation to Lane-Keeping Assistance Systems

Breyer, F.; Blaschke, Christoph; Färber, Berthold; Freyer, Jörn; Limbacher, Reimund

doi:10.1109/mits.2010.938533

Cited by 17 publications

(14 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No differences in terms of workload were found between Manual and Band, presumably because Band and Manual were largely identical when driving inside the lane, with no haptic guidance offered in the Band condition on average for 84% of the time. The lack of BA effect for driving with bandwidth guidance is in line with a comprehensive field study which focused on BA conducted by Breyer et al (2010). This study did not find evidence of BA for driving with a corrective steering system after a prolonged exposure to the system.…”

Section: Effectiveness Of the Band Systemsupporting

confidence: 82%

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

Melman

Winter

Abbink

2017

Accident Analysis & Prevention

View full text Add to dashboard Cite

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...

show abstract

Section: Effectiveness Of the Band Systemsupporting

confidence: 82%

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

Melman

Winter

Abbink

2017

Accident Analysis & Prevention

View full text Add to dashboard Cite

show abstract

“…We found 41 studies that supported a task at the control level, namely curve negotiation, lane keeping, or car-following [5], [6], [10]- [12], [14], [15], [19], [20], [30]- [33], [37], [39], [40], [42], [45]- [52], [55]- [57], [59], [60], [62], [65]- [67], [69], [70], [74], [76]- [78].…”

Section: Control Levelmentioning

confidence: 99%

“…Specifically, Brandt et al [41] combined lane keeping with obstacle avoidance, Flemisch et al [38] combined lane keeping with obeying the speed limit, and Adell et al [27] combined collision avoidance with obeying speed limit. c) Channel: The channels that were most frequently used by haptic support systems were the steering wheel [5], [11], [12], [15], [19], [20], [28], [29], [32], [37], [41]- [43], [46]- [50], [55]- [57], [64]- [67], [69], [70], [72], [73], [75], [77], gas pedal [6], [17], [22], [23], [25]- [27], [34]- [36], [40], [44], [45], [68], [71], [74], seat [7], [21], [31], [51], [52], [63], and seatbelt …”

Section: Control Levelmentioning

confidence: 99%

The Effect of Haptic Support Systems on Driver Performance: A Literature Survey

Petermeijer

Abbink

Mulder

et al. 2015

IEEE Trans. Haptics

115

View full text Add to dashboard Cite

A large number of haptic driver support systems have been described in the scientific literature. However, there is little consensus regarding the design, evaluation methods, and effectiveness of these systems. This literature survey aimed to investigate: (1) what haptic systems (in terms of function, haptic signal, channel, and supported task) have been experimentally tested, (2) how these haptic systems have been evaluated, and (3) their reported effects on driver performance and behaviour. We reviewed empirical research in which participants had to drive a vehicle in a real or simulated environment, were able to control the heading and/or speed of the vehicle, and a haptic signal was provided to them. The results indicated that a clear distinction can be made between warning systems (using vibrations) and guidance systems (using continuous forces). Studies typically used reaction time measures for evaluating warning systems and vehicle-centred performance measures for evaluating guidance systems. In general, haptic warning systems reduced the reaction time of a driver compared to no warnings, although these systems may cause annoyance. Guidance systems generally improved the performance of drivers compared to non-aided driving, but these systems may suffer from after-effects. Longitudinal research is needed to investigate the transfer and retention of effects caused by haptic support systems.

show abstract

“…The minimum required robust communication range is mainly forced by the application layer. More specifically, cooperative safety applications, such as Forward Collision Warning/Avoidance (FCW/A) [2]- [4], Cooperative Adaptive Cruise Control (CACC) [5], Lane-Keep-Assistance (LKA) [6], etc., which are supposed to employ the achieved situational awareness to make proper safety and efficiency decisions, play the main role to determine this range. According to technical documents from National Highway Traffic Safety Administration (NHTSA), 300m distance is considered as the minimum required range for a generic V2V communication standard, such as DSRC [7].…”

Section: Introductionmentioning

confidence: 99%

A Driver Behavior Modeling Structure Based on Non-Parametric Bayesian Stochastic Hybrid Architecture

Mahjoub

Toghi

Fallah

2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

View full text Add to dashboard Cite

Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semiautomated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical stochastic hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a stochastic object. Nonparametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.

show abstract

Negative Behavioral Adaptation to Lane-Keeping Assistance Systems

Cited by 17 publications

References 17 publications

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

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

The Effect of Haptic Support Systems on Driver Performance: A Literature Survey

A Driver Behavior Modeling Structure Based on Non-Parametric Bayesian Stochastic Hybrid Architecture

Contact Info

Product

Resources

About