Analysis of longitudinal driving behaviors during car following situation by the driver's EEG using PARAFAC

2013 ◽  
Vol 46 (15) ◽  
pp. 415-422 ◽  
Author(s):  
Toshihito Ikenishi ◽  
Takayoshi Kamada ◽  
Masao Nagai
Keyword(s):  
Car Following ◽  
Driving Behaviors

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

Influence of Driving Behaviors on the Stability in Car Following

2019 ◽  
Vol 20 (3) ◽  
pp. 1081-1098 ◽  
Author(s):  
Yunpeng Wang ◽  
Junjie Zhang ◽  
Guangquan Lu
Keyword(s):  
Car Following ◽  
Driving Behaviors ◽  
The Stability

scholarly journals Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Naikan Ding ◽  
Linsheng Lu ◽  
Nisha Jiao
Keyword(s):  
Visual Perception ◽  
Traffic Flow ◽  
Crash Risk ◽  
Perceptual Information ◽  
Specific Flow ◽  
Car Following ◽  
Driving Behaviors ◽  
Level Data ◽  
Spatial Transferability ◽  
Model Approach

Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

scholarly journals Heterogeneity of Driving Behaviors in Different Car-Following Conditions

2016 ◽  
Vol 44 (2) ◽  
pp. 105-114 ◽  
Author(s):  
Luigi Pariota ◽  
◽  
Francesco Galante ◽  
Gennaro Nicola Bifulco ◽  
◽  
...  
Keyword(s):  
Car Following ◽  
Driving Behaviors

Incorporating Instantaneous Reaction Delay in Car-Following Models: A Hybrid Approach

2021 ◽  
pp. 036119812110152
Author(s):  
Mehdi Rafati Fard ◽  
Saeed Rahmani ◽  
Afshin Shariat Mohaymany
Keyword(s):  
Reaction Time ◽  
Road Safety ◽  
Statistical Tests ◽  
Hybrid Approach ◽  
Car Following ◽  
Driving Behaviors ◽  
Proposed Model ◽  
Traffic Performance ◽  
Fixed Parameter ◽  
Car Following Model

Car-following is considered as one of the most prevalent fundamental driving behaviors that substantially influences traffic performance as well as road safety and capacity. Drivers’ car-following behavior is affected by numerous factors. However, in practice, very few of these factors have been scrutinized, because of their latent essence and unavailability of appropriate data. Owing to its importance, drivers’ reaction time has attracted the attention of many researchers; nevertheless, it is considered as a fixed parameter in car-following models, which is far from reality. To take the variability of drivers’ reaction time into account, a flexible hybrid approach has been suggested in the present study. In the proposed structure, in the first step, the desirable acceleration of the driver is estimated by applying an equation-based car-following model. In the next step, the driver’s reaction delay in applying the calculated acceleration is estimated by an artificial neural network. The corresponding parameters are jointly estimated by applying an estimated distribution algorithm. Statistical tests indicate better performance of the hybrid model, which considers the variations of the driver’s reaction time, compared with a traditional model with fixed reaction time. Furthermore, the cross-validation results indicate better generalizability and transferability of the proposed model in action.

The impact of aggressive driving behaviors on multi-lane highway traffic flow

2018 ◽  
Vol 29 (07) ◽  
pp. 1850056 ◽  
Author(s):  
H. B. Zhu ◽  
G. Y. Chen ◽  
H. Lin ◽  
Y. J. Zhou
Keyword(s):  
Traffic Flow ◽  
High Speed ◽  
Aggressive Driving ◽  
Highway Traffic ◽  
Lane Changing ◽  
Car Following ◽  
Driving Behaviors ◽  
Evolution Pattern ◽  
Proposed Model ◽  
The Impact

A modified cellular automata traffic model is proposed to simulate four-lane traffic flow, in which drivers are classified into aggressive drivers and cautious drivers and the anticipative velocity of the adjacent vehicles is considered. Analysis from the vehicles’ evolution pattern indicates that vehicles driven by the aggressive drivers are more powerful in behaviors of lane-changing and car-following. The model is refined by using the small cell of one meter long in order to simulate the traffic flow meticulously and realistically. The results indicate that the lane-changing maneuver exhibits different property as the density varies, and it does have a significant impact on the characteristics of the surrounding traffic flow due to their interfering effects on the following vehicles. Furthermore, the phenomenon of high-speed car-following is exhibited, and the results coincide with the empirical data very well. It is shown that the proposed model is reasonable and can partially reflect the real traffic.

Does the Tempo of Music Impact Human Behavior Behind the Wheel?

2018 ◽  
Vol 60 (4) ◽  
pp. 556-574 ◽  
Author(s):  
Jordan Navarro ◽  
François Osiurak ◽  
Emanuelle Reynaud
Keyword(s):  
Road Safety ◽  
Safety Margin ◽  
Driving Performance ◽  
Background Music ◽  
Car Following ◽  
Driving Behaviors ◽  
Musical Background ◽  
Fast Tempo ◽  
The Relationship ◽  
Music Track

Objective: Assess the influence of background music tempo on driving performance. Background: Music with a fast tempo is known to increase the level of arousal, whereas the reverse is observed for slow music. The relationship between driving performance and level of arousal was expected to take the form of an inverted U-curve. Method: Three experiments were undertaken to manipulate the musical background during driving. In Experiment 1, the driver’s preferred music track played at its original and modified (plus or minus 30%) tempo were used together with the simple ticking of a metronome. In Experiment 2, music tracks of different tempos were played during driving. In Experiment 3, music tracks were categorized as arousing or relaxing based on the associated perceived level of arousal. Results: Listening to music tended to influence drivers’ performances in a car-following task by improving coherence and gain adjustments relative to the followed vehicle but simultaneously shortened the intervehicular time. Although the tempo of the music per se did not directly affect driving behavior, arousing music tracks improved drivers’ adjustments to the followed vehicle (Experiment 3). Conclusion: The tempo of the music listened to behind the wheel was not found to influence driving behaviors. However, arousing music improved drivers’ responsiveness to changes in the speed of the followed vehicle. However, this benefit was canceled out by a reduction in the drivers’ intervehicle safety margin. Application: Listening to arousing music while driving cannot be considered to improve road safety, at least in a car-following task without attentional impairments.

scholarly journals Driving Simulator Validity of Driving Behavior in Work Zones

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yanning Zhang ◽  
Zhongyin Guo ◽  
Zhi Sun
Keyword(s):  
Delay Time ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Controlled Environment ◽  
Analysis Method ◽  
Work Zones ◽  
Relative Validity ◽  
Car Following ◽  
Driving Behaviors ◽  
Driving Scenario

Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.

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