AutoCoach: An Intelligent Driver Behavior Feedback Agent with Personality-Based Driver Models

Nowadays, AI has many applications in everyday human activities such as exercise, eating, sleeping, and automobile driving. Tech companies can apply AI to identify individual behaviors (e.g., walking, eating, driving), analyze them, and offer personalized feedback to help individuals make improvements accordingly. While offering personalized feedback is more beneficial for drivers, most smart driver systems in the current market do not use it. This paper presents AutoCoach, an intelligent AI agent that classifies drivers’ into different driving-personality groups to offer personalized feedback. We have built a cloud-based Android application to collect, analyze and learn from a driver’s past driving data to provide personalized, constructive feedback accordingly. Our GUI interface provides real-time user feedback for both warnings and rewards for the driver. We have conducted an on-the-road pilot user study. We conducted a pilot study where drivers were asked to use different agent versions to compare personality-based feedback versus non-personality-based feedback. The study result proves our design’s feasibility and effectiveness in improving the user experience when using a personality-based driving agent, with 61% overall acceptance that it is more accurate than non-personality-based.