Decision support issues in automated driving systems

Machine learning and computational processing have advanced such that automated driving systems (ADSs)

are no longer a distant reality. Many automobile manufacturers have developed prototypes; however, there

exist numerous decision support issues requiring resolution to ensure mass ADS adoption. In the coming

decades, it is likely that production ADSs will only be partially autonomous. Such ADSs operate within

predetermined conditions and require driver intervention when they are violated. Since forecasts of their

20-year market penetration are relatively low, ADSs will likely operate in heterogeneous traffic characterized

by vehicles of varying autonomy levels. Under these conditions, effective decision support must consider intangible,

subjective, and emotional factors as well as influences of human cognition; otherwise, the ADS risks

driver distrust and unsatisfactory performance based on an incomplete understanding of its environment.

We survey the literature relevant to these issues, identify open problems, and propose research directions for

their resolution.