This paper explores the use of on-board vehicle data from cars equipped with advanced driver assistance features as a fitness tracker for sustainability. The approach is inspired by fitness tracking apps, which use smartwatches and other wearable devices to monitor and characterize activities. Instead of adding new vehicle sensors, the data used is from the vehicle's Controller Area Network (CAN) bus. The authors propose a sustainability dashboard that provides key metrics such as safety, fuel efficiency, and comfort, along with trends and comparisons to historical records. This dashboard aims to help drivers understand their driving habits and the impact of different driving styles on sustainability. The dashboard is designed to be user-friendly, with sections for key performance indicators (KPIs), navigation, vehicle data, and customizable features. The paper also discusses the evaluation of these metrics, including safety assessments based on time headway and time to collision (TTC), fuel efficiency calculations, and comfort evaluations using acceleration and jerk. The authors plan to extend the system to include more comprehensive analysis and address limitations such as the availability of certain signals and environmental uncertainties. The project received funding from the National Science Foundation.This paper explores the use of on-board vehicle data from cars equipped with advanced driver assistance features as a fitness tracker for sustainability. The approach is inspired by fitness tracking apps, which use smartwatches and other wearable devices to monitor and characterize activities. Instead of adding new vehicle sensors, the data used is from the vehicle's Controller Area Network (CAN) bus. The authors propose a sustainability dashboard that provides key metrics such as safety, fuel efficiency, and comfort, along with trends and comparisons to historical records. This dashboard aims to help drivers understand their driving habits and the impact of different driving styles on sustainability. The dashboard is designed to be user-friendly, with sections for key performance indicators (KPIs), navigation, vehicle data, and customizable features. The paper also discusses the evaluation of these metrics, including safety assessments based on time headway and time to collision (TTC), fuel efficiency calculations, and comfort evaluations using acceleration and jerk. The authors plan to extend the system to include more comprehensive analysis and address limitations such as the availability of certain signals and environmental uncertainties. The project received funding from the National Science Foundation.