Gaussian Process-Based Model Predictive Control for Overtaking

by   Wenjun Liu, et al.

This paper proposes a novel framework for addressing the challenge of autonomous overtaking and obstacle avoidance, which incorporates the overtaking path planning into Gaussian Process-based model predictive control (GPMPC). Compared with the conventional control strategies, this approach has two main advantages. Firstly, combining Gaussian Process (GP) regression with a nominal model allows for learning from model mismatch and unmodeled dynamics, which enhances a simple model and delivers significantly better results. Due to the approximation for propagating uncertainties, we can furthermore satisfy the constraints and thereby safety of the vehicle is ensured. Secondly, we convert the geometric relationship between the ego vehicle and other obstacle vehicles into the constraints. Without relying on a higherlevel path planner, this approach substantially reduces the computational burden. In addition, we transform the state constraints under the model predictive control (MPC) framework into a soft constraint and incorporate it as relaxed barrier function into the cost function, which makes the optimizer more efficient. Simulation results reveal the usefulness of the proposed approach.


page 8

page 10


Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing

A fundamental aspect of racing is overtaking other race cars. Whereas pr...

Autonomous Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee using Control Barrier Functions

This paper presents a novel planning and control strategy for competing ...

Safe and Robust Motion Planning for Dynamic Robotics via Control Barrier Functions

Control Barrier Functions (CBF) are widely used to enforce the safety-cr...

Learning based Predictive Error Estimation and Compensator Design for Autonomous Vehicle Path Tracking

Model predictive control (MPC) is widely used for path tracking of auton...

Reflected Schrödinger Bridge: Density Control with Path Constraints

How to steer a given joint state probability density function to another...

Safe Hierarchical Model Predictive Control and Planning for Autonomous Systems

Planning and control for autonomous vehicles usually are hierarchical se...

Whole-Body MPC and Dynamic Occlusion Avoidance: A Maximum Likelihood Visibility Approach

This paper introduces a novel approach for whole-body motion planning an...