Scenario Authoring for a Driving Simulator to Evaluate Driver Experience in Intelligent Autonomous Vehicles

Manawadu, Udara; Ishikawa, Masaaki; Kamezaki, Mitsuhiro; Sugano, Shigeki · 2015 · Crossref

DOI: 10.1299/jsmeicam.2015.6.94

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Summary

This study addresses the need to evaluate driver experience in intelligent autonomous vehicles as they approach commercial availability. The authors developed a driving simulator to compare subjective workload and preference between conventional (human-driven) and autonomous driving modes across various traffic conditions. The motivation stems from the anticipated shift in the driver-vehicle relationship and the need for a controlled, economical method to assess these differences before widespread deployment. The researchers built a simulator using the Unity game engine, featuring a virtual environment with four distinct scenarios: expressway, urban area, rural/residential area, and parking lot. The simulator supported two control interfaces: a Logitech G27 steering wheel and pedals for conventional driving, and a Microsoft Surface Pro 3 touch-screen interface for autonomous driving, where users selected a destination and the vehicle navigated using an A* search algorithm and dynamic obstacle avoidance. Twelve participants, divided into "novice" (0–2 years experience) and "experienced" (2–8 years experience) groups, completed trials in both modes. Specific events, such as roadwork closures, sudden stops, and pedestrian incursions, were triggered during the second trial of each mode. Data collection included task completion time, collision counts, and subjective evaluations using the NASA Task Load Index (NASA-TLX) and preference questionnaires. The results demonstrated significant advantages for autonomous driving in terms of efficiency and safety. Completion time decreased by approximately 18.3% in autonomous mode, and the number of collisions was zero due to the vehicle's obstacle avoidance capabilities. Subjective workload, measured via NASA-TLX, was substantially lower in autonomous driving, with a 41.3% reduction for novices and a 49.1% reduction for experienced drivers across all parameters (mental, physical, temporal, performance, effort, and frustration). Regarding preference, both groups favored autonomous driving in urban traffic and parking scenarios, citing ease and safety. However, preferences diverged in expressway and rural/residential areas: experienced drivers preferred conventional driving, while novices preferred autonomous driving. The study concludes that autonomous driving offers superior performance in time efficiency, safety, and reduced workload compared to conventional driving. The findings highlight that driver experience level influences preference in specific scenarios, suggesting that user acceptance may vary based on driving expertise and context. The authors suggest future work should expand the participant pool to include professionals, elderly, and disabled drivers to further generalize these findings. This research provides a validated framework for using simulators to assess human factors in autonomous vehicle adoption.

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Topics

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• situational awareness
• automation surprise
• simulator sickness

Information type

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• Methodological Resource: tool software, validation psychometrics
• Theoretical Contribution: computational model