Traffic accidents worldwide cause over one million deaths per year, and over 30,000 deaths per year in the U.S. alone. Despite steadily increasing safety standards for automobiles and road construction, distracted driving, intoxicated driving, driver incompetence or inability, dangerous roads and weather conditions, high traffic roads, and extensive road commutes remain as perpetual factors in the lack of a perspicuous decline in traffic-related deaths and injuries. The advent of autonomous vehicle technology—along with the persistent increases in machine learning and artificial intelligence technology—may prove to circumvent many of the unfortunate factors that lead to traffic accidents.
Widespread concerns regarding autonomous vehicles on public roads typically relate to the ability of such vehicles to make safe and trustworthy decisions when confronted with complex situations. On a typical journey, an autonomous vehicle may encounter countless decision-making instances where loss of life is possible—however unlikely. Road intersections include traffic signaling systems that can range from simple three-bulb faces to complex directional and yielding signals. Safe, reliable, skillful, and responsible decision-making by autonomous vehicles at any intersection is necessary in order to advance the public use of autonomous vehicles and eventually prevent the vast majority of traffic accident types occurring in present road environments.