Learn More
— This paper presents our approach to TORCS Car Racing Competition 2009, it is based on a complete modular architecture capable of driving automatically a car along a track with or without oppents. The architecture is composed of five simple modules being each one responsible for a basic aspect of car driving. The modules control gear shiftings, steer(More)
First, we describe the competition regulations and the software framework. Then, the five best teams describe the methods of computational intelligence they used to develop their drivers and the lessons they learned from the participation in the championship. The organizers provide short summaries of the other competitors. Finally, we summarize the(More)
—Research on intelligent transport systems (ITSs) is steadily leading to safer and more comfortable control for vehicles. Systems that permit longitudinal control have already been implemented in commercial vehicles, acting on throttle and brake. Nevertheless, lateral control applications are less common in the market. Since a too-sudden turn of the(More)
— The techniques of Soft Computing are recognized as having a strong learning and cognition capability as well as good tolerance to uncertainty and imprecision. These properties allow them to be applied successfully to Intelligent Transportation Systems (ITS), a broad range of diverse technologies that designed to answer many transportation problems. The(More)
This paper presents a case study in which an autonomous vehicle must cooperate with a supposedly manually driven one to carry out a crossroads manoeuvre without risk. The main difference with other intersection systems is that the manual vehicle is driven without paying attention to the controlled one, so a cooperative coordination between vehicles is not(More)
Throughout the history, Genetic Algorithms (GA) have been widely applied to a broad range of combinatorial optimization problems. Its easy applicability to areas such as transport or industry has been one of the reasons for its great success. In this paper, we propose a new Adaptive Multi-Crossover Population Algorithm (AMCPA). This new technique changes(More)
We propose a multi-crossover and adaptive island based population algorithm (MAIPA). This technique divides the entire population into subpopulations, or demes, each with a different crossover function, which can be switched according to the efficiency. In addition, MAIPA reverses the philosophy of conventional genetic algorithms. It gives priority to the(More)