Lessons in Cooperation: Driver Sentiments towards
Learned Congestion Mitigation Advisors

Abstract

Congestion Mitigation systems are crucial in ad- dressing the many adverse effects of congestion such as increased commute times and emission rates. Learned Congestion Mitigation Advisors (CMAs), that provide drivers with instructions, have proven to be an effective stand-in while autonomous driving technology matures. However, the interactions between these CMAs and the drivers who they advise have not been studied extensively. To this end, we conduct a driving simulator study (N=16) to capture driver reactions to learned CMAs. We qualitatively analyze the sentiments of drivers towards learned CMAs and discuss driver preferences for various aspects of the interaction. In this work, we comment on how the advice should be communicated, the effects of the advice on driver trust, and how drivers adapt to the system. We present recommendations to inform the future design of Cooperative Advisory systems.