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Developments in autonomous vehicles (AVs) are rapidly advancing and will inthe next 20 years become a central part to our society. However, especially inthe early stages of deployment, there is expected to be incidents involvingAVs. In the event of AV incidents, decisions will need to be made that requireethical decisions, e.g., deciding between colliding into a group of pedestriansor a rigid barrier. For an AV to undertake such ethical decision making andpath planning, simulation models of the situation will be required that areused in real-time on-board the AV. These models will enable path planning andethical decision making to be undertaken based on predetermined collisioninjury severity levels. In this research, models are developed for the pathplanning and ethical decision making that predetermine knowledge regarding thepossible collision injury severities, i.e., peak deformation of the AVcolliding into the rigid barrier or the impact velocity of the AV collidinginto a pedestrian. Based on such knowledge and using fuzzy logic, a novelnonlinear weighted utility cost function for the collision injury severitylevels is developed. This allows the model-based predicted collision outcomesarising from AV peak deformation and AV-pedestrian impact velocity to beexamined separately via weighted utility cost functions with a commonstructure. The general form of the weighted utility cost function exploits afuzzy sets approach, thus allowing common utility costs from the two separateutility cost functions to be meaningfully compared. A decision-makingalgorithm, which makes use of a utilitarian ethical approach, ensures that theAV will always steer onto the path which represents the lowest injury severitylevel, hence utility cost to society.

Predicting Autonomous Vehicle Collision Injury Severity Levels for Ethical Decision Making and Path Planning