CANdid - An Open-Access Annotated Dataset of Vehicle CAN Bus Traffic

The data formats used by controller area networks (CAN) to transmit information are not strictly defined and vary across vehicle manufacturers; the same information differs in how it is encoded between vehicles. Understanding how these implementations vary requires access to various different examples of real CAN data. While publicly accessible CAN datasets do exist, each set has practical limitations when trying to use such data to examine the landscape of real-world CAN. With limited information of a vehicle's actions available, and with datasets in many cases only featuring a single vehicle, using such data to obtain a broad understanding of CAN is challenging. In this paper we present CANdid, a publicly available set of CAN data captured from ten individual vehicles. The data presented demonstrates vehicles operating in real world conditions, as well as under carefully controlled conditions with clearly labelled actions. The data also includes video footage of the driver's actions, as well as GPS information, from which a detailed understanding of the vehicle's actions during capture may be derived. We demonstrate two case studies using the data. The first uses the provided labels for actions to automatically identify vehicle components in the CAN data, and the second study uses the GPS information in the training of a machine-learning model to identify a vehicle turning from raw CAN data. With this dataset available, researchers may gain a more comprehensive understanding of CAN's various implementations across manufacturers, and how information may differ in presentation between vehicles.