Is Too Much System Caution Counterproductive? Effects of Varying Sensitivity and Automation Levels in Vehicle Collision Avoidance Systems

要旨

Autonomous vehicle system performance is limited by uncertainties inherent in the driving environment and challenges in processing sensor data. Engineers thus face the design decision of biasing systems toward lower sensitivity to potential threats (more misses) or higher sensitivity (more false alarms). We explored this problem for Automatic Emergency Braking systems in Level 3 autonomous vehicles, where the driver is required to monitor the system for failures. Participants (N=48) drove through a simulated suburban environment and experienced detection misses, perfect performance, or false alarms. We found that driver vigilance was greater for less-sensitive braking systems, resulting in improved performance during a potentially fatal failure. In addition, regardless of system bias, greater levels of autonomy resulted in significantly worse driver performance. Our results demonstrate that accounting for the effects of system bias on driver vigilance and performance will be critical design considerations as vehicle autonomy levels increase.

受賞
Honorable Mention
キーワード
Autonomous Vehicles
Automated Emergency Braking
Human Machine Interaction
Simulation
Controlled Experiment
著者
Ernestine Fu
Stanford University, Stanford, CA, USA
Mishel Johns
Stanford University, Stanford, CA, USA
David A. B. Hyde
University of California, Los Angeles, Los Angeles, CA, USA
Srinath Sibi
Stanford University, Stanford, CA, USA
Martin Fischer
Stanford University, Stanford, CA, USA
David Sirkin
Stanford University, Stanford, CA, USA
DOI

10.1145/3313831.3376300

論文URL

https://doi.org/10.1145/3313831.3376300

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Vehicle automation, pedestrians & interaction

Paper session
306AB
5 件の発表
2020-04-27 23:00:00
2020-04-28 00:15:00
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