Comparative Study on Vehicle Dynamics Behavior Using different Types of Controllers in Intersection Management Systems

Abstract

Autonomous Intersection Management (AIM) controllers develop a distributed cooperative control logic to determine conflict-free trajectories for Connected Autonomous Vehicles (CAVs) in signal-free intersections. The work in this paper aims to allow AIM systems to work within narrow margins of error resulting in increased traffic throughput and reducing traffic congestion. The cooperative trajectory planning problem is formulated as vehicle-level mixed-integer non-linear programs that aim to minimize travel time of each vehicle and their speed variations while avoiding near-crash conditions. This paper implements and tests various dynamic velocity control strategies for vehicles within an intersection. Moreover, Model Predictive Controller (MPC), Fuzzy Logic and Proportional-Integral-Differential (PID) controllers were used and compared in terms of controller effort and velocity tracking. A Comparison is formulated based on different control parameters i.e., time response characteristics and control effort. The simulations have been implemented using unreal engine and RoadRunner. The simulation results have shown an acceptable performance for all controllers under test with varying features that has been discussed throughout this study.