Using Automated Vehicle Data as a Fitness Tracker for Sustainability

TL;DR

This paper explores using vehicle onboard data as a sustainability fitness tracker, enabling drivers to understand how their driving habits impact environmental metrics without additional sensors.

Contribution

It introduces a novel approach to assess driving sustainability using existing vehicle CAN bus data, inspired by fitness tracking technology, to help drivers understand their environmental impact.

Findings

Developed indicators for key driving characteristics.

Enabled drivers to contextualize driving habits in terms of sustainability.

Laid groundwork for future driver feedback integration.

Abstract

This work describes the use of on-board vehicle data from cars with advanced driver assistance features as a trip summary, with the goal of helping drivers contextualize their driving habits in terms of sustainability. The approach is similar to recent advancements in fitness tracking apps, which leverage smartwatches and other wearable devices to characterize activities during a workout or as part of daily fitness monitoring. Instead of adding new vehicle sensors, the data used for this work is from on-board driving data, namely, signals decoded from the vehicle's Controller Area Network (CAN) bus. With the deepening research of automatic driving technologies, Autonomous Vehicles (AVs) have gradually entered the consumer field, and more users are benefiting from the convenience and safety assistance provided by driving assistance and autonomous driving. However, various technical obstacles persist due to the complex environment, the non-communication of technologies, and users' trust. We propose indicators for evaluating the key characteristics of each drive, to facilitate drivers' familiarity with advanced driver assistance systems and to allow them to consider how different driving styles affect sustainability metrics. Further extensions will allow users to add feedback as part of the driving summary, laying a data foundation for future controller iterations based on real driving data and the attitude of drivers towards it.