COLLISION AVOIDANCE PERFORMANCE ANALYSIS OF A VARIED LOADS AUTONOMOUS VEHICLE USING INTEGRATED NONLINEAR CONTROLLER

Abstract

This paper analyzes and studies the effect of varied vehicle loads to the collision avoidance
(CA) system performance. The design comprises of Artificial Potential Field as the risk
assessment and motion planning strategy and Nonlinear Model Predictive Control (NMPC) as
the automated motion guidance. The study is important to determine robust NMPC weighting
parameters for the vehicle states in varied loads vehicle collision avoidance situations.
Simulation of the proposed system was done and evaluated. The results showed that the varied
loads of the host vehicle affect the vehicle states error penalization. The findings will be helpful
for a real-time implementation of a multi-scenario highway CA system to provide a well-tuned
avoidance actuation by NMPC. It is done by identifying the most mercurial vehicle dynamics
states in all variations of vehicle loads during CA navigations.