为了在光线较差、缺失明显道路标志时，快速准确地检测道路边界，基于最大熵原理，提出了一种利用2D激光测距仪的实时测量数据快速提取路边算法。该算法首先通过对连续有限不同角度观测数据的重新组合，直接构造模糊聚类中心；然后利用最大熵原理求取当前状态的隶属度函数值，同时对测量结果和预测误差进行预测，以便消除传统方法对专家知识或先验信息的依赖；最后根据当前状态与所选连续状态的距离，利用较近状态对预测结果产生较大影响的原理来构造加权因子，并计算模糊预测结果，再利用预测误差对预测结果进行修正，这种预测误差仅由有限几次测量结果决定，而不随时间的推移产生积累。实验结果表明，该方法是有效的。

A quick road-boundary detection algorithm using 2D laser rangefinder based on maximum entropy principle is proposed. By reclassifying observed measurements at different measure angle in limited continuous domain, we calculate fuzzy cluster centers directly and predict the states and predictable error simultaneously, and calculate membership values of current state based on the maximum entropy principle. The method eliminates the dependence of conventional methods on hnowledge of experts and prior information. The weighted factors are designed for calculating prediction results based on the fact that later states have more impacts on the prediction. In the process, the error of every step is also predicted to modify the prediction results of states, and the prediction error is determined by the selected states in the limit domain. Thus the accumulation error overtime is eliminated. The results of experiments indicate the effectiveness of the proposed algorithm.