A recognition method for drivers intention based on genetic algorithm and ant colony optimization
new recognition method for drivers 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 drivers intention recognition. The experimental result shows that the recognition method and the hybrid algorithm are feasible and effective.
genetic algorithm ant colony optimization hybrid drivers intention recognition
ZHOU Shenpei WU Chaozhong
The School of Automation Wuhan University of Technology Wuhan, P. R. China Intelligent Transportation Systems Center Engineering Research Center for Transportation Safety, Min
国际会议
2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)
上海
英文
1050-1054
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）