Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Evolving competitive car controllers for racing games with neuroevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Robust player imitation using multiobjective evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
On-line neuroevolution applied to the open racing car simulator
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Artificial intelligence in racing games
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Learning drivers for TORCS through imitation using supervised methods
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
A modular parametric architecture for the TORCS racing engine
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Evolving a fuzzy controller for a car racing competition
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Controller for TORCS created by imitation
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Evolving driving controllers using genetic programming
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Optimized sensory-motor couplings plus strategy extensions for the TORCS car racing challenge
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Driving faster than a human player
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
A fuzzy aid rear-end collision warning/avoidance system
Expert Systems with Applications: An International Journal
On-line learning of a fuzzy controller for a precise vehicle cruise control system
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In car racing, blocking refers to maneuvers that can prevent, disturb or completely block an overtaking action by an incoming car. In this paper, we present an advanced overtaking behavior that is able to deal with opponents implementing blocking strategies of various difficulty level. The behavior we developed has been integrated in an existing fuzzy-based architecture for driving simulated cars and tested using The Open Car Racing Simulator (TORCS). We compared a driver implementing our overtaking strategy against four of the bots available in the TORCS distribution and Simplix, a state-of-the-art driver which won several competitions. The comparison was carried out against opponents implementing three blocking strategies of increasing difficulty and two different scenarios: (i) a basic scenario with one opponent on a straight stretch to overtake as quickly as possible; (ii) an advanced scenario involving a race on a non-trivial track against several opponents. The results from the basic scenario show that our strategy can always overtake the opponent car; in particular, our strategy is slightly more risky than the other ones and may result in a little damage, however, all the other controllers show a more careful and safe policy that often prevents them to complete an overtaking maneuver. When racing against several opponents on complex tracks, our strategy results in the best trade-off between the time spent being blocked by an opponent ahead and the number of overtaking maneuvers completed.