驾驶员加速度分布特性及其应用

Acceleration distribution characteristics of the driver and its application

为提高智能汽车的类人驾驶能力,使用自然驾驶数据(NDD)研究了驾驶员的加速行为特性。利用约5 800万观测数据组成的数据库,探讨了驾驶员加速度分布的收敛性;使用多维核密度估计得到了驾驶员加速度分布,并使用相对熵描述不同数据量的数据集之间的差异;使用稳定收敛的数据集分析了驾驶员的加速度分布特性,提取了驾驶员加速度分布的特征参数;讨论了驾驶员加速度分布在智能汽车中的应用。结果表明:驾驶员的纵向加速度和侧向加速度二维分布服从双三角形分布特征;纵向加速度和侧向加速度随速度增大而先增大后减小;驾驶员加速度分布特性可以应用于智能汽车驾驶能力测试、智能汽车安全测试、人机共驾控制、危险估计算法等方面。

The accelerating behavior characteristics of the driver were studied by using the naturalistic driving data(NDD) to improve the human-like driving ability of the intelligent vehicle. The database composed of approximately 58 million observation data was applied to discuss the convergence of the acceleration distribution of the driver. The multivariate kernel density function was used to achieve the acceleration distribution of the driver. The relative entropy(Kullback-Leibler divergence) was employed to describe the distinction among the datasets which were composed of different amount of data. The acceleration distribution of the driver was analyzed, and its parameters were achieved by using the convergent dataset. The application of the acceleration distribution characteristics of the driver in intelligent vehicle were discussed. The results show that the 2-dimensional distribution between the longitudinal acceleration and lateral acceleration follows the dual triangle distribution pattern. The longitudinal acceleration and lateral acceleration firstly increase and then decrease when the velocity increases. The acceleration distribution can be applied in the intelligent vehicle driving capability evaluation, intelligent vehicle safety test, co-driving control, risk assessment algorithm, etc.