Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handling Using Torque Vectoring

TL;DR

The paper addresses obstacle avoidance at the limit of handling by introducing a nonlinear Model Predictive Contouring Control (MPCC) that integrates torque vectoring with a nonlinear double-track vehicle model and an extended Fiala tyre model. The MPCC optimizes per-wheel longitudinal forces and steering to prioritize collision avoidance, formalized as $J = J_{track} + J_{inp} + J_{obs}$ in a Cartesian framework, and enforces constraints within the tyre friction circle. Key contributions are the first MPCC extended with torque vectoring for collision avoidance in a double-lane change, and an extended Fiala tyre model that captures vertical and longitudinal load effects on cornering stiffness, validated in high-fidelity simulations. The approach demonstrates improved obstacle avoidance and vehicle stability, achieving larger yaw moments (up to ~2000 Nm) and lower peak sideslip, indicating practical potential for safer automated driving under emergency scenarios.

Abstract

This paper presents an original approach to vehicle obstacle avoidance. It involves the development of a nonlinear Model Predictive Contouring Control, which uses torque vectoring to stabilise and drive the vehicle in evasive manoeuvres at the limit of handling. The proposed algorithm combines motion planning, path tracking and vehicle stability objectives, prioritising collision avoidance in emergencies. The controller's prediction model is a nonlinear double-track vehicle model based on an extended Fiala tyre to capture the nonlinear coupled longitudinal and lateral dynamics. The controller computes the optimal steering angle and the longitudinal forces per each of the four wheels to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Thanks to the optimisation of the longitudinal tyre forces, the proposed controller can produce an extra yaw moment, increasing the vehicle's lateral agility to avoid obstacles while keeping the vehicle stable. The optimal forces are constrained in the tyre friction circle not to exceed the tyres and vehicle capabilities. In a high-fidelity simulation environment, we demonstrate the benefits of torque vectoring, showing that our proposed approach is capable of successfully avoiding obstacles and keeping the vehicle stable while driving a double-lane change manoeuvre, in comparison to baselines lacking torque vectoring or collision avoidance prioritisation.

Figures (7)

• Figure 1: Instant of the vehicle controlled by the proposed MPCC during an evasive manoeuvre employing the torque vectoring capabilities.
• Figure 2: Double-track vehicle model.
• Figure 3: Fig. \ref{['fig:FialaFz']} compares the high-fidelity Delft, the classic Fiala and the extended Fiala tyre model with different normal loads. Fig. \ref{['fig:FialaFx']} compares the previously mentioned models with the lateral and longitudinal force coupling.
• Figure 4: Fig. \ref{['fig:FyFront']} and \ref{['fig:FyRear']} show the extended Fiala tyre model experimental validation for the front and rear axle.
• Figure 5: Experimental model validation of the vehicle characteristics for a skidpad manoeuvre with 40m radius: (a) understeer gradient, (b) yaw rate and (c) sideslip angle.