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3 articles
Article 9 September 2024
Joris Jaguemont, Ali Darwiche and Fanny Bardé
320 Views97 Downloads
Article 13 February 2024
Piotr Gorzelanczyk and Henryk Tylicki
Highlights of Vehicles
Volume 2 (2024), Issue 1, pp. 1–12
Volume 2 (2024), Issue 1, pp. 1–12
1067 Views268 Downloads
Article 26 July 2023
Maksym Diachuk and Said M. Easa
This article is part of the Special Issue Feature Papers to the Inaugural Volume of Highlights of Vehicles.
Highlights of Vehicles
Volume 1 (2023), Issue 1, pp. 29–53
Volume 1 (2023), Issue 1, pp. 29–53
1556 Views488 Downloads1 Citations
Article 26 July 2023
Maksym Diachuk and Said M. Easa
The study aims at improving the technique of planning the autonomous vehicles’ (AV) speed mode based on a kinematic model with physical restrictions. A mathematical model relates the derivatives of kinematic parameters with ones of the
The study aims at improving the technique of planning the autonomous vehicles’ (AV) speed mode based on a kinematic model with physical restrictions. A mathematical model relates the derivatives of kinematic parameters with ones of the trajectory’s curvature. The inverse approach uses an expanded vehicle model considering the distribution of vertical reactions, wheels’ longitudinal reactions according to a drive type, and lateral forces ensuring motion stability. For analysis of the drive type, four options are proposed: front-wheel drive (FWD), rear-wheel drive (RWD), permanent engaged all-wheel drive (AWD), and 4-wheel drive with torque vectoring (4WD-TV). The optimization model is also built by the inverse scheme. The longitudinal speed’s higher derivatives are modeled by the finite element (FE) functions with nodal unknowns. The sequential integrations ensure the optimality and smoothness of the third derivative. The kinematic restrictions are supplemented by the tire-road critical slip states. Sequential quadratic programming (SQP) and the Gaussian N-point scheme for quadrature integration are used to minimize the objective function. The simulation results show a significant difference in the mode forecasts between four types of AV drives at the same initial conditions. This technique allows redistributing the traction forces strictly according to the wheels’ adhesion potentials and increases the optimization performance by about 40% compared to using the kinematic model based on the same technique without physical constrains.
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This article is part of the Special Issue Feature Papers to the Inaugural Volume of Highlights of Vehicles.
Highlights of Vehicles
Volume 1 (2023), Issue 1, pp. 29–53
Volume 1 (2023), Issue 1, pp. 29–53
1556 Views488 Downloads1 Citations
Volume 2 (2024), Issue 2, pp. 24–34