Dynamic Risk Management for Safely Automating Connected Driving Maneuvers

Autonomous vehicles (AV)s have the potential for significantly improving road safety by reducing the number of accidents caused by inattentive and unreliable human drivers. Allowing the AVs to negotiate maneuvers and to exchange data can further increase traffic safety and efficiency. Simultaneously, these improvements lead to new classes of risk that need to be managed in order to guarantee safety. This is a challenging task since such systems have to face various forms of uncertainty that current safety approaches only handle through static worst-case assumptions, leading to overly restrictive safety requirements and a decreased level of utility. This work provides a novel solution for dynamic quantification of the relationship between uncertainty and risk at run time in order to find the trade-off between system's safety and the functionality achieved after the application of risk mitigating measures. Our approach is evaluated on the example of a highway overtake maneuver under consideration of uncertainty stemming from wireless communication channels. Our results show improved utility while ensuring the freedom of unacceptable risks, thus illustrating the potential of dynamic risk management.