Citation

Pryde M, Nehaoua L, Hadj-Abdelkader H, Arioui H. 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV); April 3-6, 2023; Abstract #: 23-0225-W, pp. 13p. Washington, DC USA: US National Highway Traffic Safety Administration, 2023 open access.

Affiliation

Laboratoire IBISC, Universit´e d’Evry Val-d’Essonne, Universit´e Paris-Saclay France

Copyright

(Copyright © 2023 open access, US National Highway Traffic Safety Administration)

Abstract

27th International Technical Conference on the Enhanced Safety of Vehicles (ESV): Enhanced and Equitable Vehicle Safety for All: Toward the Next 50 Years

https://www-esv.nhtsa.dot.gov/Proceedings/27/27ESV-000225.pdf

The authors propose a visual-inertial algorithm to estimate the kinematic states of a motorcycle traveling at high speeds along an extra-urban road. The approach comprises the following steps: First, a monocular camera takes video of the road ahead. Key features from sequential video frames of the road surface are extracted using the Harris corner detector. Matching features are identified using the Fast retina keypoint descriptor (FREAK). Next, correct the perspective warping of the feature locations by applying inverse perspective mapping. The motion of the transformed features is registered using the Singular Value Decomposition (SVD) variant of the Iterative Closest Point (ICP) algorithm. Finally, this measurement is combined with readings from inertial navigation system using a Kalman filter to produce a filtered estimate and correct integrator drift. The approach was validated using data from simulations of three scenarios created in BikeSim. In the first, the motorcycle performs a series of slaloms along a straight road at 50 km/h. In the second, the motorcycle navigates an S-shaped bend at 80 km/h. Lastly, the motorcycle performs a double-lane change across both lanes of a straight road at 110 km/h.

Language: en