Driving examiners' views on data-driven assessment of test candidates: An interview study. (November 2021)

• Record Type: Journal Article
• Authors: Driessen, Tom; Picco, Angèle; Dodou, Dimitra; de Waard, Dick; de Winter, Joost
• Is Part Of: Transportation research. Volume 83(2022)
• Page Start: 60
• Page End: 79
• Publication Date: 2021-11
• Subjects: Semi-structured interview -- Driving test -- Driving license -- Data -- Driver assessment -- Decision aid -- Driving simulator; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2021.09.021
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: The growing amount of sensors in cars offer opportunities for the driving license test. Thirty-seven driving examiners were interviewed about data-driven assessment. The examiners wanted to use data to clarify their verdict to the candidate. The examiners proposed eye movements, speed, distance, braking data, and videos. The examiners were skeptical about decision aids that determine the pass/fail verdict. Abstract: Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about data-driven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behavior, and color-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were skeptical about higher levels of decision support, noting that forming an overall picture of the candidate's abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test.