  Developments in autonomous vehicles (AVs) are rapidly advancing and will in
the next 20 years become a central part to our society. However, especially in
the early stages of deployment, there is expected to be incidents involving
AVs. In the event of AV incidents, decisions will need to be made that require
ethical decisions, e.g., deciding between colliding into a group of pedestrians
or a rigid barrier. For an AV to undertake such ethical decision making and
path planning, simulation models of the situation will be required that are
used in real-time on-board the AV. These models will enable path planning and
ethical decision making to be undertaken based on predetermined collision
injury severity levels. In this research, models are developed for the path
planning and ethical decision making that predetermine knowledge regarding the
possible collision injury severities, i.e., peak deformation of the AV
colliding into the rigid barrier or the impact velocity of the AV colliding
into a pedestrian. Based on such knowledge and using fuzzy logic, a novel
nonlinear weighted utility cost function for the collision injury severity
levels is developed. This allows the model-based predicted collision outcomes
arising from AV peak deformation and AV-pedestrian impact velocity to be
examined separately via weighted utility cost functions with a common
structure. The general form of the weighted utility cost function exploits a
fuzzy sets approach, thus allowing common utility costs from the two separate
utility cost functions to be meaningfully compared. A decision-making
algorithm, which makes use of a utilitarian ethical approach, ensures that the
AV will always steer onto the path which represents the lowest injury severity
level, hence utility cost to society.
