Driving Behavior Statistical Characteristics of the Driver
CSTR:
Author:
Affiliation:

Clc Number:

U471.3

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    In this paper, the driving behavior statistical characteristics of the driver are studied by using the naturalistic driving data. The longitudinal acceleration, lateral acceleration, yaw rate, and velocity of the vehicle were chosen as the characteristic parameters which were employed to describe the driving behavior of the driver. Firstly, the convergence of the driving behavior of the driver was discussed. The kernel density estimation was used to achieve the probability distribution of the driving behavior characteristic parameters. And the kullback-liebler divergence was applied to describe the distribution distinction between datasets which were composed of different amount of data. Next, the distribution characteristics of the driving behavior characteristic parameters were proposed by using the convergent dataset. In the last, the conditional distribution of the driving behavior characteristic parameters were used to study the interaction between these parameters. The conclusions can be summarized as: The forward acceleration, brake deceleration, lateral acceleration, and yaw rate approximately follow the Pareto distribution. The steering maneuver of the driver tends to be more intense when brake deceleration or forward acceleration increases, and vice versa. The steering, braking, and accelerating maneuvers of the driver become more intense and then become less intense when the velocity increases.

    Reference
    Related
    Cited by
Get Citation

LIU Rui, MA Zhixiong, WU Biao, ZHU Xichan. Driving Behavior Statistical Characteristics of the Driver[J].同济大学学报(自然科学版),2019,47(06):0832~0841

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:June 25,2018
  • Revised:March 31,2019
  • Adopted:March 04,2019
  • Online: July 03,2019
  • Published:
Article QR Code