Online Time-Optimal Trajectory Planning on Three-Dimensional Race Tracks

Rowold, Matthias; Ögretmen, Levent; Kasolowsky, Ulf; Lohmann, Boris

doi:10.1109/iv55152.2023.10186701

Cited by 11 publications

(1 citation statement)

References 18 publications

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“…Most studies proceed from the control strategy regarding minimizing the motion time while keeping the vehicle within roadway boundaries [9]. Thus, in [10], the time-optimal trajectory around a racetrack is obtained by solving a minimum lap time problem (MLTP) for a racing vehicle. In [11], the path planning strategy includes preventing rear-end collisions during overtaking while minimizing traveling time.…”

Section: Introductionmentioning

confidence: 99%

Planning Speed Mode of All-Wheel Drive Autonomous Vehicles Considering Complete Constraint Set

Diachuk,

Easa

2024

Vehicles

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The study aims to improve the technique of motion planning for all-wheel drive (AWD) autonomous vehicles (AVs) by including torque vectoring (TV) models and extended physical constraints. Four schemes for realizing the TV drive were considered: with braking internal wheels, using a rear-axle sport differential (SD), with braking front internal wheel and rear-axle SD, and with SDs on both axles. The mathematical model combines 2.5D vehicle dynamics model and a simplified drivetrain model with the self-locking central differential. The inverse approach implies optimizing the distribution of kinematic parameters by imposing a set of constraints. The optimization procedure uses the sequential quadratic programming (SQP) technique for the nonlinear constrained minimization. The Gaussian N-point quadrature scheme provides numerical integration. The distribution of control parameters (torque, braking moments, SDs’ friction moment) is performed by evaluating linear and nonlinear algebraic equations inside of optimization. The technique proposed demonstrates an essential difference between forecasts built with a pure kinematic model and those considering the vehicle’s drive/control features. Therefore, this approach contributes to the predictive accuracy and widening model properties by increasing the number of references, including for actuators and mechanisms.

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Section: Introductionmentioning

confidence: 99%