Robust Vehicle State Estimation from Urban Driving to the Limits of Handling

A study conducted by Daiki Mori et. al. was published in the IEEE Transactions on Intelligent Transportation Systems.

High-precision vehicle position, velocity, and attitude estimation on any terrain with full-range vehicle motion is the next step to improve the availability of advanced vehicle safety and autonomous systems. Accurately estimating vehicle motion from very low speeds to beyond the limits of handling, not only asphalt but also snow using automotive-grade sensors presents a significant challenge.

This paper presents a robust vehicle state estimation algorithm that covers full-range vehicle motion under various road and maneuvering conditions. A strap-down navigation system, an aerospace-oriented approach, is adopted to cover every possible motion of the vehicle while a stochastic vehicle dynamics constraint is used to further improve robustness. Additionally, unknown sensor delays are estimated to improve the algorithm’s scalability. The position, velocity, and attitude errors were less than 0.1 m, 0.1 m/s, and 0.1 deg, respectively, both on dry and on ice surface with up to 40 deg of sliding angle. The proposed algorithm is expected to enhance the safety and improve the availability of autonomous vehicle safety systems.

Title: Robust Vehicle State Estimation from Urban Driving to the Limits of Handling
Authors: Mori, D., Kamekawa, M., Fujieda, N., Mizuno, Y.
Journal Name: IEEE Transactions on Intelligent Transportation Systems
Published: November 15, 2024 (online)
https://doi.org/10.1109/TITS.2024.3487117