Title
Driving as a human: a track learning based adaptable architecture for a car racing controller
Abstract
We present the evolution and current state of the Mr. Racer car racing controller that excelled at the corresponding TORCS competitions of the last years. Although several heuristics and black-box optimization methods are employed, the basic idea of the controller architecture has been to take over many of the mechanisms human racing drivers apply. They learn the track geometry, plan ahead, and wherever necessary, adapt their plans to the current circumstances quickly. Mr. Racer consists of several modules that have partly been adapted and optimized separately, where the final tuning is usually done with respect to a certain racing track during the warmup phase of the TORCS competitions. We also undertake an experimental evaluation that investigates how the controller profits from adding some of the modules to a basic configuration and which modules are most important for reaching the best possible performance.
Year
DOI
Venue
2014
10.1007/s10710-014-9227-z
Genetic Programming and Evolvable Machines
Keywords
Field
DocType
Car racing,Evolutionary computation,TORCS,Planning controller
Architecture,Control theory,Simulation,Computer science,Evolutionary computation,Heuristics,Track geometry,Car racing,Controller architecture
Journal
Volume
Issue
ISSN
15
4
1389-2576
Citations 
PageRank 
References 
4
0.55
31
Authors
3
Name
Order
Citations
PageRank
Jan Quadflieg1436.52
Preuss Mike293381.70
Günter Rudolph321948.59