  Automated vehicles have received much attention recently, particularly the
DARPA Urban Challenge vehicles, Google's self-driving cars, and various others
from auto manufacturers. These vehicles have the potential to significantly
reduce crashes and improve roadway efficiency by automating the
responsibilities of the driver. Still, automated vehicles are expected to crash
occasionally, even when all sensors, vehicle control components, and algorithms
function perfectly. If a human driver is unable to take control in time, a
computer will be responsible for pre-crash behavior. Unlike other automated
vehicles--such as aircraft, where every collision is catastrophic, and guided
track systems, which can only avoid collisions in one dimension--automated
roadway vehicles can predict various crash trajectory alternatives and select a
path with the lowest damage or likelihood of collision. In some situations, the
preferred path may be ambiguous. This study investigates automated vehicle
crashing and concludes the following: (1) automated vehicles will almost
certainly crash, (2) an automated vehicle's decisions preceding certain crashes
will have a moral component, and (3) there is no obvious way to effectively
encode complex human morals in software. A three-phase approach to developing
ethical crashing algorithms is presented, consisting of a rational approach, an
artificial intelligence approach, and a natural language requirement. The
phases are theoretical and should be implemented as the technology becomes
available.
