Honorable Mention
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.
https://doi.org/10.1145/3313831.3376300
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)