Self-Perception Versus Objective Driving Behavior: Subject Study of Lateral Vehicle Guidance

TL;DR

This work addresses the challenge of aligning autonomous vehicle driving styles with human preferences to improve comfort and acceptance. It combines a controlled on-road study (N=$62$) on rural German roads with a German translation of the Multidimensional Driving Style Inventory (MDSI-DE) and introduces new objective indicators for lateral curve negotiation, including stationary and transient cornering analyses and the G-G envelope representation. The study finds only modest correlations between self-reported driving styles and objective lateral indicators, with substantial driver heterogeneity in curve negotiation, and demonstrates that acceleration and jerk metrics drive most associations. Practically, the results suggest that personalized lateral driving functions in AVs will require richer, curve-specific indicators beyond standard self-reports to enhance user comfort and adoption; the authors also provide a publicly accessible dataset to facilitate further research.

Abstract

Advancements in technology 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 have fixed parameters, and there is no universally agreed-upon driving style for autonomous vehicles. Integrating driving style preferences into automated vehicles may enhance acceptance and reduce uncertainty, expediting their adoption. A controlled vehicle study (N = 62) was conducted with a variety of German participants to identify the individual lateral driving behavior of human drivers, specifically emphasizing rural roads. We introduce novel indicators for assessing stationary and transient curve negotiation, directly applicable in developing personalized lateral driving functions. To assess the predictability of these indicators using self-reports, we introduce the MDSI-DE, the German version of the Multidimensional Driving Style Inventory. The correlation analysis between MDSI factor scores and proposed indicators showed modest but significant associations, primarily with acceleration and jerk statistics while the in-depth lateral driving behavior turned out to be highly driver-heterogeneous. The dataset including the anonymized socio-demographics and questionnaire responses, the raw vehicle measurements including labels, and the derived driving behavior indicators are publicly available at https://www.kaggle.com/datasets/jhaselberger/spodb-subject-study-of-lateral-vehicle-guidance.

Figures (6)

• Figure 1: The used measurement track with a total length of 71.9km is located near Kempten (Allgäu), Germany and is composed of 14.5km city (orange), 20.5km highway (blue), 18.3km rural (red), and 18.6km federal (green) road segments. Each subject followed the exact same route. The track is followed counterclockwise.
• Figure 2: Determination of the G-G Envelope points. Small angle pieces of size $2\delta_r$ are rotated using $\delta_s$ to sample the acceleration points. The statistical features maximum, 95$^{th}$ percentile, 75$^{th}$ percentile, and mean are calculated for each angle bin to form the envelope. In the background, the raw measurements are shown.
• Figure 3: Stationary cornering behavior on rural roads without traffic exemplified on three randomly selected subjects. The lateral acceleration $a_y$ and the distance to the center-line $d_{CL}$ are shown on the x-axis and y-axis. The curve-cutting gradient is derived from a linear regression of the stationary cornering behavior. While the first and third subjects cut the curve, the second subject tends to drift outward.
• Figure 4: Examples of recorded trajectories with associated four-digit codes and derived trajectory classes. The left curves are shown in the first row, and the right curves in the second row. All curves are projected onto a straight road surface. The center band is illustrated in gray, and the theoretical lane center as a dashed line. Curve entries and exists are shaded with light gray. The driven distance is displayed on the x-axis and the lateral deviations on the y-axis. The curve-cutting intensity is visualized by the light red shaded areas. The outermost two lines represent the lane boundaries.
• Figure 5: Distribution of driving styles determined from the MDSI questionnaire divided into women and men. Within each gender group, all six driving styles are represented.