The document describes the GECCO 2013 Simulated Car Racing competition. It provides details on the competition framework, including the use of the TORCS simulator, sensors and effectors for the simulated cars, and the competition software. It describes the qualifying process where controllers raced on 3 unknown tracks and were scored based on distance covered. The top 8 controllers then progressed to the races, where they competed on the same tracks in 8 races each to determine the final ranking. Mr. Racer was the winner based on its total points across the races.

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

2. GECCO 2013 Simulated Car Racing Competition
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.

3. Competition Framework

4. GECCO 2013 Simulated Car Racing Competition
The Open Racing Car Simulator
& the Competition Software
TORCS
BOT BOT BOT
TORCS
PATCH
SBOT SBOT SBOT
BOT BOTBOT
UDP UDPUDP
q The competition server
q Separates the bots from TORCS
q Build a well-defined sensor model
q Works in real-time

5. GECCO 2013 Simulated Car Racing Competition
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

6. Competitors

7. GECCO 2013 Simulated Car Racing Competition
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

8. GECCO 2013 Simulated Car Racing Competition
Industrial Computer Science Department.
Centro de Automática y Robótica
Consejo Superior de Investigaciones Científicas
Madrid, Spain
Contact:E. Onieva (enrique.onieva@car.upm-csic.es)
AUTOPIA

9. GECCO 2013 Simulated Car Racing Competition
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)

10. GECCO 2013 Simulated Car Racing Competition
EVOR (Evolutionary Racer)
Samadhi Nallaperuma, Frank Naumann (supervisor)
University of Adelaide, Austrailia

11. GECCO 2013 Simulated Car Racing Competition
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

12. GECCO 2013 Simulated Car Racing Competition
Ahoora Driver
Mohammad reza Bonyadi, Samadhi Nallaperuma,
Zbigniew Michalewicz, and Frank Neumann
University of Adelaide, Australia

13. GECCO 2013 Simulated Car Racing Competition
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)

14. GECCO 2013 Simulated Car Racing Competition
THE GAZELLE - Adaptive Car Pilot
Dana Vrajitoru and Kholah Albelihi
Indiana University South Bend

15. GECCO 2013 Simulated Car Racing Competition
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

16. GECCO 2013 Simulated Car Racing Competition
GRN Driver
Stéphane Sanchez & Sylvain Cussat-Blanc
University of Toulouse
FRANCE

17. GECCO 2013 Simulated Car Racing Competition
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]

18. GECCO 2013 Simulated Car Racing Competition
Tetsuo Shirakawa, Show Nakamura, and Ruck Thawonmas
Intelligent Computer Entertainment Laboratory
Ritsumeikan University
ICER-IDDFS

19. GECCO 2013 Simulated Car Racing Competition
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

20. GECCO 2013 Simulated Car Racing Competition
Jan Quadflieg, Tim Delbruegger, Kai Verlage and Mike Preuss
TU Dortmund
Mr. Racer

21. GECCO 2013 Simulated Car Racing Competition
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

22. Presto AI
SCR Competition Entry 2013
Uwe Kadritzke

23. 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

24. GECCO 2013 Simulated Car Racing Competition
SnakeOil
Chris X Edwards

25. GECCO 2013 Simulated Car Racing Competition
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

26. Qualifying

27. GECCO 2013 Simulated Car Racing Competition
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

28. Alsoujlak

29. GECCO 2013 Simulated Car Racing Competition
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

30. Arraias

31. GECCO 2013 Simulated Car Racing Competition
Qualifying: Arraias
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
MrRacer
ICER-­‐IDDFS
AUTOPIA
GRNDriver
Presto
AI
SnakeOil
EVOR
GAZELLE
Ahoora

32. Sancassa

33. GECCO 2013 Simulated Car Racing Competition
Qualifying: Sancassa
0
2000
4000
6000
8000
10000
12000
MrRacer
ICER-­‐IDDFS
AUTOPIA
GRNDriver
Presto
AI
SnakeOil
EVOR
GAZELLE
Ahoora

34. GECCO 2013 Simulated Car Racing Competition
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

35. GECCO 2013 Simulated Car Racing Competition
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
Removed from the race

36. Races

37. GECCO 2013 Simulated Car Racing Competition
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

38. GECCO 2013 Simulated Car Racing Competition
Competitor Alsoujlak Arraias Sancassa Total
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

39. Thank you!
SCR Contacts
Official Webpage
http://scr.geccocompetitions.com
Email
scr@geccocompetitions.com
Google Group
http://groups.google.com/group/racingcompetition