Self-perception versus objective driving behavior: Subject study of lateral vehicle guidance

• Published in: Transportation research. Part F, Traffic psychology and behaviour Vol. 109; pp. 272 - 298
• Main Authors: Haselberger, Johann, Schick, Bernhard, Müller, Steffen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Driving behavior; Human factors; Instrumented vehicle; MDSI; Self-reports; Subject study; Driving behavior; Self-reports; MDSI; Subject study; Instrumented vehicle; Human factors
• ISSN: 1369-8478
• DOI: 10.1016/j.trf.2024.12.012

Technological advances are steering attention toward creating comfortable and acceptable driving characteristics in autonomous vehicles. Ensuring a safe and comfortable ride experience is vital for the widespread adoption of autonomous vehicles, as mismatches in driving styles between humans and autonomous systems can impact passenger confidence. Current driving functions possess fixed parameters, and a universally agreed-upon driving style for autonomous vehicles does not exist. Integrating driving style preferences into automated vehicles may enhance acceptance and reduce uncertainty, expediting their adoption. A controlled subject study (N=62) focusing on human factors was conducted with a variety of German participants to identify the individual lateral driving behavior of human drivers, specifically emphasizing rural roads. Vehicle and environment-dependent signals were collected during real-world drives with an instrumented vehicle on a predefined ▪ route. These signals included acceleration and jerk values and the distance to the lane-center. A set of original indicators for analyzing stationary and transient curve negotiation are introduced, directly applicable in developing personalized lateral driving functions. The MDSI-DE, the German version of the Multidimensional Driving Style Inventory, is used to evaluate the predictability of these indicators using self-reports. The results demonstrate that self-reported driving styles can manifest in specific driving behaviors, with statistically significant correlations found mainly with acceleration and jerk values. However, they do not accurately reflect detailed lateral driving behaviors such as curve cutting. Hence, objective indicators for online driving style estimation benefit autonomous vehicle personalization. The gathered dataset is publicly available at https://www.kaggle.com/datasets/jhaselberger/spodb-subject-study-of-lateral-vehicle-guidance. •Conduction of a controlled real-world driving study (N=62).•Introduction of driving style indicators for evaluating lateral driving behavior.•Self-assessed driving style primarily aligns with acceleration and jerk statistics.•In-depth lateral driving behavior turned out to be highly driver-heterogeneous.•Publicly accessible provision of the dataset.