Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS

TL;DR

This paper investigates various automotive speed sensors, their error characteristics, and proposes methods to identify sensor types and model errors, enhancing speed estimation accuracy for navigation in GNSS-challenged environments.

Contribution

It introduces a novel approach to identify sensor types and model their errors, improving speed estimation in automotive navigation systems.

Findings

Sensor fusion improves speed estimation accuracy.

Error modeling varies across sensor types.

Data collected from real urban driving scenarios supports validation.

Abstract

Modern on-road navigation systems heavily depend on integrating speed measurements with inertial navigation systems (INS) and global navigation satellite systems (GNSS). Telemetry-based applications typically source speed data from the On-Board Diagnostic II (OBD-II) system. However, the method of deriving speed, as well as the types of sensors used to measure wheel speed, differs across vehicles. These differences result in varying error characteristics that must be accounted for in navigation and autonomy applications. This paper addresses this gap by examining the diverse speed-sensing technologies employed in standard automotive systems and alternative techniques used in advanced systems designed for higher levels of autonomy, such as Advanced Driver Assistance Systems (ADAS), Autonomous Driving (AD), or surveying applications. We propose a method to identify the type of speed sensor in a vehicle and present strategies for accurately modeling its error characteristics. To validate our approach, we collected and analyzed data from three long real road trajectories conducted in urban environments in Toronto and Kingston, Ontario, Canada. The results underscore the critical role of integrating multiple sensor modalities to achieve more accurate speed estimation, thus improving automotive navigation state estimation, particularly in GNSS-denied environments.