Model-based Path Planning and Control for Autonomous Vehicles using Artificial Potential Fields ...

144 Pages·2017·6.67 MB·English

Model-based Path Planning and
Control for Autonomous Vehicles
using Artificial Potential Fields
E.Y. Snapper
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Delft Center for Systems and Control

Model-based Path Planning and
Control for Autonomous Vehicles
using Artificial Potential Fields
Master of Science Thesis
For the degree of Master of Science in Systems and Control at Delft
University of Technology
E.Y. Snapper
January 10, 2018
Faculty of Mechanical, Maritime and Materials Engineering (3mE) · Delft University of
Technology
The work in this thesis was supported by the Nederlandse organisatie voor Toegepast Natu-
urwetenschappelijk Onderzoek (TNO). Their cooperation is hereby gratefully acknowledged.
Copyright © Delft Center for Systems and Control (DCSC)
All rights reserved.
Delft University of Technology
Department of
Delft Center for Systems and Control (DCSC)
The undersigned hereby certify that they have read and recommend to the Faculty of
Mechanical, Maritime and Materials Engineering (3mE) for acceptance a thesis
entitled
Model-based Path Planning and Control for Autonomous Vehicles
using Artificial Potential Fields
by
E.Y. Snapper
in partial fulfillment of the requirements for the degree of
Master of Science Systems and Control
Dated: January 10, 2018
Supervisor(s):
prof.dr.ir. J. Hellendoorn
dr.ir. M. Alirezaei
dr.ir. E. Semsar-Kazerooni
Reader(s):
First Reader
Second Reader

Abstract
This report presents the results of the graduation thesis from the TU Delft, performed at the
Integrated Vehicle Safety (IVS) department of TNO. The goal of this graduation project is
to develop a unified path-planning and -tracking method for autonomous vehicles in highway-
driving scenarios, by making use of Artificial Potential Fields (APFs).
Mostautonomousvehiclesbasetheirnavigationcontrolonfirstplanningapath,whichisthen
tracked by using a combination of feedback and feedforward control. The strength of using
APFs is the possibility to integrate the path-planning and -tracking process. This concept
has already been extensively used in the field of robotics. The attractive and repulsive forces
coming from the APF guide the robot towards the final goal while avoiding obstacles. This
offers an intuitive way to represent the level of hazard experienced in the direct environment.
However, considerably less research has been devoted to the application of APFs in the field
of autonomous vehicles. Furthermore, the available research mostly treats the vehicle as a
particle, thereby leaving out the more complicated vehicle dynamics.
Therefore, this research is aimed at including the vehicle dynamics into the path-planning
process such as to generate feasible and desirable paths. A Model Predictive Control (MPC)
framework is proposed to fulfill this task. The adopted vehicle model is given by the linear
bicyclemodel,whichrepresentsthevehicledynamicssufficientlywellforhighwayapplications.
The two main maneuvers of lane keeping and lane changing are executed with the aid of two
different potential fields that were designed for these specific purposes: the road and obstacle
APF,respectively. AsecondorderTaylorapproximationisusedtoincorporatetheAPFsinto
the quadratic MPC cost function. The Simulink model from TNO to simulate the controlled
vehicle is modified by extending it to curved roads and by including the developed APF
MPC controller. The resulting algorithm is capable of following curved highway lanes and
overtaking slower vehicles at different velocities in the simulation environment. The results
arecomparedwiththepreviouslydevelopedpathplannerandlateralcontrollerfromTNO. In
order to also deliver longitudinal control action, the model can be connected to the Adaptive
Cruise Control (ACC) application of TNO.
It is concluded that the suggested method potentially offers a powerful solution to the navi-
gation control of autonomous vehicles. Real-life experiments have to be done in the future to
validate the performance of the controller.
Master of Science Thesis E.Y. Snapper
ii
E.Y. Snapper Master of Science Thesis
Table of Contents
Preface xi
1 Introduction 1
1-1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1-2 Literature study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1-3 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Autonomous driving 7
2-1 Autonomous vehicle system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2-2 Driver behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2-3 Highway environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2-4 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2-5 Vehicle maneuvers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2-6 Collision avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3 Controller design 17
3-1 Model predictive control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3-2 Vehicle model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3-3 Potential field functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3-3-1 Road potential field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3-3-2 Obstacle potential field . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3-3-3 Artificial potential field . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3-4 Combining APF with MPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4 Simulations 49
4-1 TNO controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4-2 Quadratic programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4-3 Weight tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4-4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4-5 Experimental considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Master of Science Thesis E.Y. Snapper
iv Table of Contents
5 Conclusions 65
5-1 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
A Bicycle model derivation 69
B Quadratic Taylor approximation 73
C MPC matrix derivation 79
D Simulink model 85
E Tuned obstacle PF parameters 91
F Lane-change characteristics results 99
Bibliography 111
Glossary 117
List of Acronyms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
List of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Index 125
E.Y. Snapper Master of Science Thesis