User-Centered Explainable Artificial Intelligence in Partially Automated Vehicles

Artificial Intelligence (AI) has been integrated into emerging technologies, such as automated vehicles (AVs), to help the vehicle sense the driving environment and make maneuvers. However, AI is imperfect and susceptible to errors. In partially AVs, the human driver is still needed to oversee the vehicle’s actions and take over when AI makes an incorrect identification. Explainable AI is needed for drivers to better calibrate their understanding of AI’s capabilities. The goal of the current study was to determine what AI explanation best supports drivers’ mental models and understanding of the partially AV when encountering various driving actions and road signs. Thirty-six participants were included in this 6 (driving scenario) × 3 (timing of the explanation) within-subjects design. Drivers’ understanding and subjective evaluations of the explanations were collected for 18 trials. This study has significance in the fields of emerging technology in transportation, human understanding of AI, and cybersecurity.