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Objective: This study aims to compare the effectiveness and subjective acceptance of three designs for haptic lane-keeping assistance in truck driving. Background: Haptic lane-keeping assistance provides steering torques toward a reference trajectory, either continuously or only when exceeding a bandwidth. These approaches have been previously investigated in driving simulators, but it is unclear how these generalize toward real-life truck driving. Method: Three haptic lane-keeping algorithms to assist truck drivers were evaluated on a 6.3-km-long oval-shaped test track: (1) a single-bandwidth (SB) algorithm, which activated assistance torques when the predicted lateral deviation from lane center exceeded 0.4 m; (2) a double-bandwidth (DB) algorithm, which activated as SB, but deactivated after returning within 0.15 m lateral deviation; and (3) an algorithm providing assistance torques continuously (Cont) toward the lane center. Fifteen participants drove four trials each, one trial without and one for each haptic assistance design. Furthermore, participants drove with and without a concurrent visually distracting task. Results: Compared to unsupported driving, all three assistance systems provided similar safety benefits in terms of decreased absolute lateral position and number of lane departures. Participants reported higher satisfaction and usability for Cont compared to SB. Conclusion: The continuous assistance was better accepted than bandwidth assistance, a finding consistent with prior driving simulator research. Research is still needed to investigate the long-term effects of haptic assistance on reliance and after-effects. Application: The present results are useful for designers of haptic lane-keeping assistance, as driver acceptance and performance are determinants of reliance and safety, respectively.

Original languageEnglish
Number of pages16
JournalHuman Factors
DOIs
Publication statusE-pub ahead of print - 2020

    Research areas

  • driver acceptance, driver distraction, haptic shared control, lane keeping, truck driving

ID: 74303637