Comparison of self-report and objective measures of driving behavior and road safety: A systematic review

Kaye, Sherrie-Anne; Lewis, Ioni; Freeman, James E.

doi:10.1016/j.jsr.2018.02.012

Cited by 49 publications

(21 citation statements)

References 22 publications

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“…In spite of the fact that the reliability and usefulness of such self-reported measures of driving behaviours have been demonstrated by multiple recent studies (e.g. [8,64]), such studies may still retain some potential weaknesses, such as recall bias and self-serving bias [33], which to some extent make the reports of an individual's personal driving patterns less trustworthy. This study has attempted to address this issue by inviting participants to drive the simulator, and by performing context-based interviews to gain new and in-depth insights of those parameters which can be used to define driving styles and which should be incorporated into a personalised AV system.…”

Section: Design Implicationsmentioning

confidence: 99%

Exploring Personalised Autonomous Vehicles to Influence User Trust

Sun

Tang

et al. 2020

Cogn Comput

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Trust is a major determinant of acceptance of an autonomous vehicle (AV), and a lack of appropriate trust could prevent drivers and society in general from taking advantage of such technology. This paper makes a new attempt to explore the effects of personalised AVs as a novel approach to the cognitive underpinnings of drivers' trust in AVs. The personalised AV system is able to identify the driving behaviours of users and thus adapt the driving style of the AV accordingly. A prototype of a personalised AV was designed and evaluated in a lab-based experimental study of 36 human drivers, which investigated the impact of the personalised AV on user trust when compared with manual human driving and non-personalised AVs. The findings show that a personalised AV appears to be significantly more reliable through accepting and understanding each driver's behaviour, which could thereby increase a user's willingness to trust the system. Furthermore, a personalised AV brings a sense of familiarity by making the system more recognisable and easier for users to estimate the quality of the automated system. Personalisation parameters were also explored and discussed to support the design of AV systems to be more socially acceptable and trustworthy.

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Section: Design Implicationsmentioning

confidence: 99%

Exploring Personalised Autonomous Vehicles to Influence User Trust

Sun

Tang

et al. 2020

Cogn Comput

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“…Previously DEA was used to calculate the road safety index for 21 European countries [16] and for ideal trauma management regarding road safety [21]. For the calculation of risk value for European countries: population, passenger-kilometers, and passenger cars were used as inputs and the number of fatalities as output [22,94]. Yearly comparative performance of road safety program was evaluated by the DEA technique [23,94].…”

Section: Road Accident Risk Index and Benchmarkingmentioning

confidence: 99%

“…Because of that reason, hotspot identification of accident-prone locations remains a focal point of interests for transportation engineers. That is why a series of researches were involved in relationship analysis of accidents [12][13][14][15][16][17][18][19][20][21][22][23] in context of volume/capacity (V/C) [18,19,24,25], vehicles miles travelled (VMT) [17,[26][27][28][29], vehicles hours travelled (VHT) [25,30,31], speed [18,20,24,[32][33][34][35][36][37][38], flow [12,13,32,35,36,39], and geometric design [35,[40][41][42][43][44]. Different methods were applied to locate the risky road segments to be treated for safety level enhancement.…”

Section: Introductionmentioning

confidence: 99%

Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making

Shah

Ahmad

2019

Applied Sciences

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Road infrastructure sustainability is directly associated with the safety of human beings. As a transportation engineer and policymaker, it is necessary to optimize the funding mechanism for road safety improvement by identifying problematic road segments. Infrastructure improvement is one of the key targets for efficient road safety management. In this study, data envelopment analysis (DEA) technique has been applied in combination with a geographical information system (GIS) to evaluate the risk level of problematic segments of a 100 km-long motorway (M-2) section. Secondly, the cross efficient method has been used to rank the risky segments for prioritization and distribution of funding to improve the road safety situation. This study will help in efficiently identifying the risky segments for safety improvement and budget allocation prioritization. GIS map will further improve the visualization and visibility of problematic segments to easily locate the riskiest segments of the motorway.

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“…Even though psychology conditions do not affect the driving actions directly, they can be used as indicators of the current physical status of the driver [14], which is because psychology conditions significantly affect judgment in the driving process. Physiological indicators, including the electroencephalogram (EEG) [15], electrocardiogram (ECG) [16], electromyography (EMG) [17], heart rate [18], eye gaze distribution [19], saccade range, blink frequency [20], and so on, were used to measure drivers’ physical status. Data were collected either in the field driving environment or on a driving simulator [21].…”

Section: Introductionmentioning

confidence: 99%

A Driver’s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model

Wang

et al. 2019

Sensors

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Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers’ physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers’ physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R–R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi’an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies.

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Comparison of self-report and objective measures of driving behavior and road safety: A systematic review

Cited by 49 publications

References 22 publications

Exploring Personalised Autonomous Vehicles to Influence User Trust

Exploring Personalised Autonomous Vehicles to Influence User Trust

Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making

A Driver’s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model

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