A recognition method for driver's intention based on genetic algorithm and ant colony optimization

Abstract

A new recognition method for driver's intention is proposed in this study. Genetic algorithm (GA) has strong adaptability, robustness and quick global searching ability. It has such disadvantages as premature convergence, low convergence speed and so on. Ant colony optimization (ACO) converges on the optimization path through pheromone accumulation and renewal. It has the ability of parallel processing and global searching and the characteristic of positive feedback. But the convergence speed of ACO is lower at the beginning for there is only little pheromone difference on the path at that time. The hybrid algorithm of genetic algorithm and ant colony optimization adopts genetic algorithm to give pheromone to distribute. And then it makes use of ant colony optimization to give the precision of the solution. It develops enough advantage of the two algorithms. The comparative analysis on optimal performance is made by using the Camel function. Finally, the method is used for the optimized the decision tree of driver's intention recognition. The experimental result shows that the recognition method and the hybrid algorithm are feasible and effective.