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

2024-12-31

AI Summary

In GNSS-challenged environments (e.g., tunnels) and due to heterogeneity among vehicle-mounted speed sensors—such as wheel-speed encoders, CAN-bus-derived velocity, and GNSS/INS-fused estimates—existing speed estimation methods suffer from large errors and poor cross-platform robustness. Method: This paper first systematically classifies and models the error characteristics of diverse automotive speed sensors; proposes a data-driven sensor-type identification method and a hierarchical error modeling strategy; and designs a multi-source heterogeneous sensor fusion framework enabling high-precision synchronization and online calibration of OBD-II, CAN, GNSS, and INS data. Contribution/Results: Evaluated on long-distance real-world trajectories across multiple cities, the proposed approach reduces speed estimation error by 42% under GNSS-denied conditions, significantly enhancing the robustness and accuracy of state estimation in navigation and autonomous driving systems.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

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.

Problem

• Vehicle Speed Estimation
• GPS Signal Degradation
• Speed Sensor Error

Innovation

• Sensor Fusion
• Speed Estimation
• Autonomous Driving