Comparing the Effects of False Alarms and Misses on Humans' Trust in (Semi)Autonomous Vehicles

Hebert Azevedo-Sa, Suresh Kumaar Jayaraman, Connor T. Esterwood, X. Jessie Yang, Lionel P. Robert, Dawn M. Tilbury

Abstract: Trust in automated driving systems is crucial for effective driver-(semi)autonomous vehicles interaction. Drivers that do not trust the system appropriately are not able to leverage its benefits. This study presents a mixed design user experiment where participants conducted a non-driving task while traveling in a simulated semi-autonomous vehicle with forward collision alarm and emergency braking functions. Occasionally, the system missed obstacles or provided false alarms. We varied these system error types as well as road shapes, and measured the effects of these variations on trust development. Results reveal that misses are more harmful to trust development than false alarms, and that these effects are strengthened by operation on risky roads. Our findings provide additional insight into the development of trust in automated driving systems, and are useful for the design of such technologies.