Driving Behavior Characteristics on Urban Expressway On- and Off-Ramp by Simulation

2020 ◽  
pp. 1309-1323
Author(s):  
Cai Xin ◽  
Zhong Yi ◽  
Zhao Yong ◽  
Mao Yan
Keyword(s):  
Driving Behavior ◽  
Urban Expressway ◽  
Behavior Characteristics

A Graph Based Security Description Method of Driving Behavior Characteristics

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Hang Qi ◽  
Xiao-Hua Zhao ◽  
Yi-Ping Wu ◽  
Chang Liu
Keyword(s):  
Driving Behavior ◽  
Behavior Characteristics

Study on Individual Driving Style Based on Driving Behavior Characteristics

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Yuan-yuan Ren ◽  
Xian-sheng Li ◽  
Xue-lian Zheng
Keyword(s):  
Driving Behavior ◽  
Driving Style ◽  
Behavior Characteristics

scholarly journals A Comparative Study of Accident Risk Related to Speech-Based and Handheld Texting during a Sudden Braking Event in Urban Road Environments

2020 ◽  
Vol 17 (16) ◽  
pp. 5675 ◽  
Author(s):  
Rui Fu ◽  
Yunxing Chen ◽  
Qingjin Xu ◽  
Yuxi Guo ◽  
Wei Yuan
Keyword(s):  
Mobile Phone ◽  
Driving Behavior ◽  
Initial Time ◽  
Accident Risk ◽  
Urban Road ◽  
Time Headway ◽  
Young Drivers ◽  
Deceleration Rate ◽  
Accident Probability ◽  
Behavior Characteristics

The use of mobile phones while driving is a very common phenomenon that has become one of the main causes of traffic accidents. Many studies on the effects of mobile phone use on accident risk have focused on conversation and texting; however, few studies have directly compared the impacts of speech-based texting and handheld texting on accident risk, especially during sudden braking events. This study aims to statistically model and quantify the effects of potential factors on accident risk associated with a sudden braking event in terms of the driving behavior characteristics of young drivers, the behavior of the lead vehicle (LV), and mobile phone distraction tasks (i.e., both speech-based and handheld texting). For this purpose, a total of fifty-five licensed young drivers completed a driving simulator experiment in a Chinese urban road environment under five driving conditions: baseline (no phone use), simple speech-based texting, complex speech-based texting, simple handheld texting, and complex handheld texting. Generalized linear mixed models were developed for the brake reaction time and rear-end accident probability during the sudden braking events. The results showed that handheld texting tasks led to a delayed response to the sudden braking events as compared to the baseline. However, speech-based texting tasks did not slow down the response. Moreover, drivers responded faster when the initial time headway was shorter, when the initial speed was higher, or when the LV deceleration rate was greater. The rear-end accident probability respectively increased by 2.41 and 2.77 times in the presence of simple and complex handheld texting while driving. Surprisingly, the effects of speech-based texting tasks were not significant, but the accident risk increased if drivers drove the vehicle with a shorter initial time headway or a higher LV deceleration rate. In summary, these findings suggest that the effects of mobile phone distraction tasks, driving behavior characteristics, and the behavior of the LV should be taken into consideration when developing algorithms for forward collision warning systems.

Simulated Analysis of Modeling of Driving Behavior Characteristics Based on Satellite Positioning Data

2019 ◽  
Vol 23 (1) ◽  
pp. 114-118
Author(s):  
Pei Xie ◽  
Lei Deng ◽  
◽  
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
High Accuracy ◽  
Driving Behavior ◽  
Modeling Method ◽  
Road Transportation ◽  
Stability Parameters ◽  
Traffic Flow Model ◽  
Satellite Positioning ◽  
Behavior Characteristics

To promote the road transportation security, it’s necessary to study the modeling method of driving behavior characteristics. The traffic flow model realized by current modeling methods of driving behavior characteristics has a low accuracy in warning results. Therefore, based on satellite positioning data, a modeling method of driving behavior characteristics is proposed in this paper. Firstly, the dynamic model and kinetic model of traffic flow are built through the flow, speed and density parameters; then the response time, minimum safe distance and stability parameters of driving behavior are taken as the identification index of driving behavior to identify the driving behavior of drivers; according to the identification results, the psychological field theory and satellite positioning data are combined to build the model of driving behavior characteristics, and finally, warning the drivers according to their psychology and the actual situation of road. Experimental results demonstrate that the proposed method can accurately measure the traffic flow and speed, and the score of drivers’ behavior obtained has high accuracy, which verified again the high accuracy of traffic flow model and warning results of the proposed method.

scholarly journals Adaptive Cruise Control System Evaluation According to Human Driving Behavior Characteristics

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 90
Author(s):  
Lin Liu ◽  
Qiang Zhang ◽  
Rui Liu ◽  
Xichan Zhu ◽  
Zhixiong Ma
Keyword(s):  
Control System ◽  
Evaluation Method ◽  
Adaptive Cruise Control ◽  
Driving Behavior ◽  
Cruise Control ◽  
Adaptive Cruise Control System ◽  
Cruise Control System ◽  
Behavior Characteristics ◽  
Test Scenarios ◽  
Testing And Evaluation

With the rapid and wide implementation of adaptive cruise control system (ACC), the testing and evaluation method becomes an important question. Based on the human driver behavior characteristics extracted from naturalistic driving studies (NDS), this paper proposed the testing and evaluation method for ACC systems, which considers safety and human-like at the same time. Firstly, usage scenarios of ACC systems are defined and test scenarios are extracted and categorized as safety test scenarios and human-like test scenarios according to the collision likelihood. Then, the characteristic of human driving behavior is analyzed in terms of time to collision and acceleration distribution extracted from NDS. According to the dynamic parameters distribution probability, the driving behavior is divided into safe, critical, and dangerous behavior regarding safety and aggressive and normal behavior regarding human-like according to different quantiles. Then, the baselines for evaluation are designed and the weights of different scenarios are determined according to exposure frequency, resulting in a comprehensive evaluation method. Finally, an ACC system is tested in the selected test scenarios and evaluated with the proposed method. The tested vehicle finally got a safety score of 0.9496 (full score: 1) and a human-like score as fail. The results revealed the tested vehicle has a remarkably different driving pattern to human drivers, which may lead to uncomfortable ride experience and user-distrust of the system.

Study on Changes of Vehicle Driving Behavior Characteristics for Alcohol Used Drivers

2014 ◽  
Vol 1030-1032 ◽  
pp. 2115-2120
Author(s):  
Ming En Zhong ◽  
Han Chi Hong ◽  
Jian Jin Cai
Keyword(s):  
Drunk Driving ◽  
Driving Behavior ◽  
Transportation Safety ◽  
Simulated Driving ◽  
Driving Behaviors ◽  
Obvious Change ◽  
Driving Speed ◽  
Frequency Probability ◽  
Behavior Characteristics

Drunk driving is proved to be dangerous for traffic transportation safety. However, there are still no specific conclusions about what changes do drinking make to each kind of driving behaviors. This paper set out a drunk states induce program and a simulated driving experiment to measure the data about driving speed, overspeed driving probability, brake frequency, probability of deceleration for avoidance and probability of running red lights when the drivers were in different degree of drunk states. Results showed that driving speed increases significantly only in state of heavy drunk while decreases slightly in state of moderate drunk. Overspeed probability grows higher as the drivers drink more. Brake frequency increases slightly in state of light drunk, but has no obvious change in state of moderate drunk, however decreases significantly in state of heavy drunk. Probability of deceleration for avoidance decreases a little in state of light drunk but significantly in state of heavy drunk, however increases in state of moderate dunk. Probability of running red lights increases significantly only in state of heavy drunk, but has no obvious change in both states of light drunk and moderate drunk. All these support a conclusion that drinking has different influences on each kind of driving behaviors, which perform differently for traffic transportation safety. Judgments for drunk driving related issues should be decided on each specific matter.

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