Real-time Adaptive Nonlinear MPC for Collision Imminent Control and Planning in Automated Vehicles

Real-time Adaptive Nonlinear MPC for Collision Imminent Control and Planning in Automated Vehicles: Enforcing constraints and utilizing the full control potential

Trip, Kenrick (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Mechanical, Maritime and Materials Engineering)

Mazo, M. (mentor)
Spaan, M.T.J. (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

2024-01-25

With the introduction of autonomous vehicles on public roads, their performance in emergency situations has become a strong focus. Collision Imminent Control (CIC) concerns the planning and control of aggressive evasive maneuvers for collision avoidance of automated vehicles. CIC is implemented using adaptive Nonlinear Model Predictive Control (NMPC), which considers obstacles and road barriers for combined trajectory re-planning and control. To achieve real-time performance, the prediction model complexity is often reduced, which can lead to an under-utilization of the control potential. The aim of CIC is to use as much of the control potential of the vehicle as possible while remaining real-time viable.

In this research, CIC is implemented using objective-based collision avoidance based on the distance to obstacles and road boundaries. Different collision avoidance formulations were derived and compared on accuracy and real-time performance. The control potential of the vehicle was further exploited by a computationally efficient vehicle model that employs differential braking. The NMPC problem is solved using Sequential Quadratic Programming (SQP) with Real Time Iterations (RTI). Different techniques that reduce the computation time were compared. Sparse solvers and variable timesteps were found to be most significant.

The robustness of the controller was improved by friction estimation. The controller is furthermore demonstrated to work on highly curved roads and in scenarios with dynamic obstacles. The controller is implemented on the hardware of a real autonomous vehicle and simulated on a closed-loop embedded system. Combining all these elements results in a CIC controller that can apply more control potential and reach control frequencies upwards of 100 Hz, increasing the level of safety in vehicle collision avoidance.

NMPC
Real-time control
Collision avoidance
Path planning
Autonomous vehicles

http://resolver.tudelft.nl/uuid:9afc5dfc-fcf7-41a1-a66e-f23b3714326c

Double degree in Mechanical Engineering | Systems and Control and Electrical Engineering | Embedded Systems

Student theses

master thesis

© 2024 Kenrick Trip