Design and evaluation of an advanced driver assistance system: the case of auto-adaptive cruise control

Tricot, N.; Rajaonah, B.; Popieul, J.-C.; Millot, P. · 2006 · Crossref

DOI: 10.3917/th.692.0129

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Summary

This study investigates the design and evaluation of an Auto-Adaptive Cruise Control (AACC) system, aiming to improve human-machine cooperation in driving by addressing the limitations of conventional Adaptive Cruise Control (ACC). Conventional ACC systems possess limited "know-how" (KH) and "know-how-to-cooperate" (KHC), often failing to account for static environmental factors like curves or villages, or individual driving styles. This deficiency can lead to negative interferences and increased driver workload. The research questions whether enhancing a system’s KH and KHC reduces perceived workload and improves cooperative behavior, and whether prior experience with ACC influences these outcomes. The experiment utilized the SHERPA driving simulator with 30 participants (aged 22–52) who completed a 60-kilometer route comprising motorways and major roads. Two AACC modes were tested: AACC1, which provided early warnings before automatic speed adjustments, and AACC2, which acted simultaneously with warnings. The study employed a between-subjects design for ACC experience (prior users vs. non-users) and AACC mode, while comparing AACC performance against data from a previous study on conventional ACC. Dependent variables included objective driving performance metrics (device usage time, deactivation frequency, parameter modifications) and subjective questionnaire scores regarding workload, cooperation efficiency, and risk perception. The results confirmed the second hypothesis: drivers with prior ACC experience exhibited significantly higher device usage rates and lower deactivation frequencies compared to inexperienced drivers, indicating that experience facilitates better cooperation and reduces perceived workload. However, the first hypothesis was not supported; the enhanced KH and KHC of the AACC did not significantly reduce workload or improve cooperation metrics compared to conventional ACC. While total AACC usage was higher than ACC usage on major roads, there was no significant difference in device usage between the AACC1 and AACC2 modes. The authors attribute the failure to confirm the first hypothesis to methodological limitations, specifically the lack of precise workload analysis and the use of a between-subjects design for the AACC modes, which prevented direct comparison of the warning systems' effects. The study concludes that the theoretical framework of Human-Machine Cooperation is applicable to automotive assistance systems. It highlights that while system intelligence is crucial, driver experience plays a more significant role in facilitating cooperation than the specific adaptive features tested. The findings suggest that future designs must more rigorously account for workload measurement and consider within-subject comparisons to fully evaluate the benefits of advanced cooperative capabilities in driver assistance systems.

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Topics

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• trust calibration
• automation surprise
• situational awareness
• automation
• eco driving
• in vehicle coaching

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• Methodological Resource: tool software
• Theoretical Contribution: computational model, theory or model