2013 Simulated Car Racing @ GECCO-2013

GECCO 2013 Simulated Car Racing Competition

GECCO 2013 Simulated Car Racing Competition Daniele Loiacono and Pier Luca Lanzi


SCR in a nutshell

q  Develop a driver for TORCS " hand-coded, " learned, " evolved, " …

q  Three races on three unknown tracks q  Each race has the following structure:

" Warm-up: each driver can explore the track and learn something useful

" Qualifiers: each driver races alone against the clock (the best 8 drivers move to the race)

" Actual race: all the drivers race together q  Drivers are scored based on their final position in the races,

best lap-time, receiving the least amount of damages.

Competition Framework


The Open Racing Car Simulator & the Competition Software

TORCS

BOT BOT BOT

TORCS PATCH

SBOT SBOT SBOT

BOT BOT BOT

UDP UDP UDP q  The competition server

q  Separates the bots from TORCS q  Build a well-defined sensor model q  Works in real-time


Sensors and actuators

q  Rangefinders for edges on the track and opponents (with noise) q  Speed, RPM, fuel, damage, angle with track, distance race, position

on track, etc.

q  Six effectors: steering wheel [-1,+1], gas pedal [0, +1], brake pedal [0,+1], gearbox {-1,0,1,2,3,4,5,6}, clutch [0,+1], focus direction

Competitors


SCR 2013 Entrants

q  State of the art: AUTOPIA, Madrid and Granada, Spain q  Entries

" EVOR, University of Adelaide, Australia " Ahoora, University of Adelaide, Australia " GAZZELLE, Indiana University South Bend, USA " GRN Driver, University of Toulouse, France " ICER-IDDFS, Ritsumeikan University, Japan " Mr.Racer, TU Dortmund, Germany " Presto AI, Uwe Kadritzke, Germany " SnakeOil, Chris X Edwards, Switzerland


Industrial Computer Science Department. Centro de Automática y Robótica Consejo Superior de Investigaciones Científicas Madrid, Spain Contact:E. Onieva ([email protected])

AUTOPIA


AUTOPIA

q  Fuzzy Architecture based on three basic modules for gear, steering and speed control " optimized with a genetic algorithm

q  Learning in the warm-up stage:

" Maintain a vector with as many real values as tracklength in meters.

" Vector initialized to 1.0 " If the vehicle goes out of the track or suffers damage

then multiply vector positions from 250 meters before the current position by 0.95.

q  During the race the vector is multiplied by F to make the driver more cautious in function of the damage: " F=1-0.02*round(damage/1000)


EVOR (Evolutionary Racer)

Samadhi Nallaperuma, Frank Naumann (supervisor) University of Adelaide, Austrailia


EVOR (Evolutionary Racer)

q  Build a track model during the Warm-up stage q  A (1+1)EA is used to evolve a controller, optimizing control

values q  Fitness is based on the evaluation of the racing line with

respect to the track model


Ahoora Driver

Mohammad reza Bonyadi, Samadhi Nallaperuma, Zbigniew Michalewicz, and Frank Neumann University of Adelaide, Australia


Ahoora Driver

q  Four main parameterized modules: " Steer controller " Speed controller " Opponent manager " Stuck manager

q  Parameters have been set using an evolutionary algorithm (a continuous space evolutionary method) for several tracks with known friction

q  During competition, parameters are adapted based on " The estimated friction " Trial and error (adaptively based on number of failures,

e.g. out of the track) q  Additional features: jump detection and management (in

steer and speed modules)


THE GAZELLE - Adaptive Car Pilot

Dana Vrajitoru and Kholah Albelihi Indiana University South Bend


The GAZELLE Adaptive Car Pilot

q  Mainly based on programmed heuristics q  Four modules:

" Target direction " Target speed " Opponent detection " Trouble spots handling

q  Depending on the current state, the opponent detection module might rise some flag to change the behaviors of other modules


GRN Driver

Stéphane Sanchez & Sylvain Cussat-Blanc

University of Toulouse FRANCE


GRN Driver

q  A Gene Regulatory Network (GRN) regulates the car steering and throttle " Proteins are encoded in a genome and are evolved by a

standard GA (optimization on 3 tracks, normal+mirrored for longest distance)

" This approach is naturally adaptative and resistant to noise (no noise filter implemented)

q  Scripted recovery behavior and driving assistance (traction control

and ABS) q  Modification of the GRN perception to learn braking zones of the

track during warm up and to handle opponents during the race

Track sensor 3 Track sensor 5 Track sensor 7 Track sensor 8 Track sensor 9 Track sensor 10 Track sensor 11 Track sensor 13 Track sensor 15

Speed X Speed Y

GRN Le; steer

Right steer

Accelerator

Brake

Steer=(le;-‐right)/(le;+right)

accelbrake=(accel-‐brake)/(accel+brake)

Normalized in [0,1]


Tetsuo Shirakawa, Show Nakamura, and Ruck Thawonmas Intelligent Computer Entertainment Laboratory Ritsumeikan University

ICER-IDDFS


q  Based on iterative deepening depth-first search for path finder and accelerator control " Select the path having the

highest evaluation points " If such a path cannot be found,

use a default module implemented according to our understanding (J) of Autopia’s one

q  Warm-up " Slow at the first loop to learn the track " Then, try both dirt and road parameters

and select the better one " Slow the speed down at every past

accident location, if any q  Use simple rules to avoid a crash

with another car q  Implement a rule to regain the car’s

balance when losing it

IDDFS


Jan Quadflieg, Tim Delbruegger, Kai Verlage and Mike Preuss TU Dortmund

Mr. Racer


Mr. Racer 2013

q  Main features " 2 * 28 Parameters learned offline with the CMA-ES " Noise handling with low pass filtering and regression " One parameter set for tarmac tracks, one for dirt tracks " Completely new opponent handling (based on bachelor thesis of

Kai Verlage) q  On-line learning during the warm-up

" Track model " Choice of parameters set " Tuning of target speed for all corners

q  Opponent handiling " A module recommends overtaking lines, blocking lines or target

speeds depending on the current situation " Recommendations become more defensive depending on

damage " Planning module incorporates recommended target speed and

racing line into the plan

Presto AI

SCR Competition Entry 2013

Uwe Kadritzke

Presto AI

l  Pure Heuristics l  Use of Physical Laws, e.g. Centripetal Force l  Inspired by Bernhard Wymann (BT Driver) l  Unfinished, buggy

Main Areas of Attention l  Steering l  Speed Control l  Dealing with Noise


SnakeOil

Chris X Edwards


SnakeOil

q  Main goal was to develop a library to encourage Python programmers to enter the SCR. It's quite easy to use to develop your own bot.

q  Mapped a complete turn by turn track description. q  Created a route plan for where to be at every point on the

track... but that didn't work and probably can't without a heroic effort. It's harder than it first seems.

q  Used the track feature map to mark trouble spots and show more caution there while racing.

q  Ready to be optimized with evolutionary algorithms

Qualifying


Scoring process: Warm-up & Qualifying

q  Scoring process involves three tracks: " Alsoujlak (hill track) " Arraias (desert track) " Sancassa (city track)

q  The tracks are not distributed with TORCS: " Generated using the Interactive Track Generator for

TORCS and Speed Dreams available at: •  http://trackgen.pierlucalanzi.net

" The competitors cannot know the tracks q  Each controller raced for 100000 game ticks in the warm-up

stage and then its performance is computed in the qualifying stage as the distance covered within 10000 game ticks

Alsoujlak


Qualifying: Alsoujlak

0.00 2000.00 4000.00 6000.00 8000.00 10000.00 12000.00

MrRacer

ICER-‐IDDFS

AUTOPIA

GRNDriver

Presto AI

SnakeOil

EVOR

GAZELLE

Ahoora

Arraias


Qualifying: Arraias

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

MrRacer

ICER-‐IDDFS

AUTOPIA

GRNDriver

Presto AI

SnakeOil

EVOR

GAZELLE

Ahoora

Sancassa


Qualifying: Sancassa

0 2000 4000 6000 8000 10000 12000

MrRacer

ICER-‐IDDFS

AUTOPIA

GRNDriver

Presto AI

SnakeOil

EVOR

GAZELLE

Ahoora


Qualifying summary (scores with noise)

Competitor Alsoujlak Arraias Sancassa Total

AUTOPIA 6 10 10 26

MrRacer 10 5 6 21

ICER-IDDFS 8 4 8 20

GRNDriver 5 8 4 17

SnakeOil 3 6 3 12

Presto AI 4 0 5 9

EVOR 2 2 2 6

GAZELLE 1 3 1 5

Ahoora 0 1 0 1

Evolutionary Approaches


Qualifying summary (scores with noise)


AUTOPIA 6 10 10 26

MrRacer 10 5 6 21

ICER-IDDFS 8 4 8 20

GRNDriver 5 8 4 17

SnakeOil 3 6 3 12

Presto AI 4 0 5 9

EVOR 2 2 2 6

GAZELLE 1 3 1 5

Ahoora 0 1 0 1

Removed from the race

Races


Three Tracks

For each track we run 8 races with different starting grids

Each race is scored using the F1 point system

(10 to first, 8 to second, 6 to third, …)

Two points to the controller with lesser damage

Two points for the fastest lap of the race



AUTOPIA 12 13 13 38

MrRacer 9 5.5 6 20.5

ICER-IDDFS 6 5 8 19

GRNDriver 5 5.5 4 14.5

SnakeOil 3 7 3.5 13.5

Presto AI 3 1 5 9

EVOR 3 4 1 8

GAZELLE 1.5 3 2 6.5

Final Results

Mr. Racer is the winner of GECCO-2013 SCR

Thank you! SCR Contacts Official Webpage http://scr.geccocompetitions.com Email [email protected] Google Group http://groups.google.com/group/racingcompetition

Date post:	08-May-2015
Category:	Technology
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2013 Simulated Car Racing @ GECCO-2013

Technology