Autonomous cars that 'think' like humans cut traffic risk by 26%

Researchers at the Hong Kong University of Science and Technology (HKUST) have developed a novel cognitive encoding framework that enables autonomous vehicles (AVs) to make decisions with human-like moral reasoning and situational awareness. Unlike current AV systems that assess risks in a limited pairwise manner, this new approach evaluates multiple road users simultaneously, prioritizing vulnerable pedestrians and cyclists through a concept called “social sensitivity.” The system ranks risks based on vulnerability and ethical considerations, allowing AVs to yield or stop for pedestrians even when traffic rules permit movement, and anticipates the impact of its maneuvers on overall traffic flow. Tested in 2,000 simulated traffic scenarios, the framework demonstrated a 26.3% reduction in total traffic risk, with pedestrian and cyclist risk exposure dropping by 51.7%, and an 8.3% risk reduction for the AVs themselves. Notably, these safety improvements were achieved alongside a 13.9% increase in task completion speed. The system’s adaptability allows it to be tailored to different regional driving norms and legal frameworks, enhancing its potential for global implementation. This breakthrough addresses critical limitations in current autonomous driving technology, promising safer streets and more socially responsible AV behavior in complex, real-world environments.