scholarly journals Effects of on-Board Unit on Driving Behavior in Connected Vehicle Traffic Flow

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xin Chang ◽  
Haijian Li ◽  
Jian Rong ◽  
Zhufei Huang ◽  
Xiaoxuan Chen ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Congestion ◽  
Positive Impact ◽  
Driving Behavior ◽  
Connected Vehicle ◽  
Trajectory Data ◽  
External Stimulation ◽  
Stable Regime ◽  
Car Following ◽  
Instrumented Vehicle

Connected vehicle technology has potentials to increase traffic safety, reduce traffic pollution, and ease traffic congestion. In the connected vehicle environment, the information interaction among people, cars, roads, and the environment is significantly enhanced, and driver behavior will change accordingly due to increased external stimulation. This paper designed a Vehicle-to-Vehicle (V2V) on-board unit (OBU) based on driving demand. In addition, a simulation platform for the interconnection and communication between the OBU and simulator was built. Thirty-one test drivers were investigated to drive an instrumented vehicle in four scenarios, with and without the OBU under two different traffic states. Collected trajectory data of the subject vehicle and the vehicle in front, as well as sociodemographic characteristics of the test drivers were used to evaluate the potential impact of such OBUs on driving behavior and traffic safety. Car-following behavior is an essential component of microsimulation models. This paper also investigated the impacts of the V2V OBU on car-following behaviors. Considering the car-following related indicators, the k-Means algorithm was used to categorize different car-following modes. The results show that the OBU has a positive impact on drivers in terms of speed, front distance, and the time to stable regime. Furthermore, drivers’ opinions show that the system is acceptable and useful in general.

Operational safety of automated and human driving in mixed traffic environments: A perspective of car-following behavior

2021 ◽  
pp. 1748006X2110506
Author(s):  
Tao Li ◽  
Xu Han ◽  
Jiaqi Ma ◽  
Marilia Ramos ◽  
Changju Lee
Keyword(s):  
Traffic Safety ◽  
Risk Mitigation ◽  
Safety Evaluation ◽  
Mixed Traffic ◽  
Trajectory Data ◽  
Road Transportation ◽  
Baseline Model ◽  
Car Following ◽  
On The Road ◽  
The Impact

The advent of automated vehicles (AVs) will provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicle (HV) to a fully AV traffic environment, there will be a mixed traffic flow including both HVs and AVs. The impact of introducing AVs into existing traffic, however, has not yet been fully understood. In this paper, we advance this understanding by conducting mixed traffic safety evaluation from the perspective of car-following behavior using real-world AV operational data of mixed traffic. To understand how the AVs impact other vehicles on the road, we analyzed the operational behaviors of HV-following-HV, AV-following-HV, and HV-following-AV. A selected car-following model is calibrated, and results show that there are significant differences between the HV-following-HV and the other two groups, indicating safe AV behavior and changes in HV behavior (i.e. less aggressive, safer) after the introduction of AVs into the traffic. Additionally, to understand AV behavioral safety, we investigate behavior predictions (one of the most critical inputs for AVs to make car-following decisions) of AVs and their surrounding vehicles using a mature baseline model and a new Conditional Variational Autoencoder (CVAE) framework. The result shows potential risks of inaccurate predictions of the baseline model and the necessity to consider additional factors, such as vehicle interactions and driver behavior, into the prediction for risk mitigation. Arterial vehicle trajectory data from the Lyft Level 5 Dataset is applied to test the proposed methodological framework to understand the car-following safety risks of HVs and AVs in the mixed traffic stream.

Car-Following Model Calibration Based on Driving Simulator Data to Study Driver Characteristics and to Investigate Model Validity in Extreme Traffic Situations

2021 ◽  
pp. 036119812110326
Author(s):  
Moritz Berghaus ◽  
Eszter Kallo ◽  
Markus Oeser
Keyword(s):  
Driving Simulator ◽  
Driver Behavior ◽  
Field Studies ◽  
Driving Behavior ◽  
Model Parameters ◽  
Prediction Errors ◽  
Trajectory Data ◽  
Car Following ◽  
Model Validity ◽  
Simulator Study

In this paper we use traffic data from a driving simulator study to calibrate four different car-following models. We also present two applications for which the calibration results can be used. The first application relied on the advantage that driving simulator data also contain information on driver characteristics, for example, age, gender, or the self-assessment of driver behavior. By calibrating the models for each driver individually, the resulting model parameters could be used to analyze the influence of driver characteristics on driver behavior. The analysis revealed that certain characteristics, for example, self-identification as an aggressive driver, were reflected in the model parameters. The second application was based on the capability to simulate dangerous situations that require extreme driving behavior, which is often not included in datasets from real traffic and cannot be provoked in field studies. The model validity in these situations was analyzed by comparing the prediction errors of normal and extreme driving behavior. The results showed that all four car-following models underestimated the deceleration in an emergency braking scenario in which the drivers were momentarily shocked. The driving simulator study was validated by comparing the calibration results with those obtained from real trajectory data. We concluded that driving simulator data were suitable for the two proposed applications, although the validity of driving simulator studies must always be regarded.

scholarly journals A Support Vector Regression Approach for Investigating Multianticipative Driving Behavior

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Lu ◽  
Shaoquan Ni ◽  
Scott S. Washburn
Keyword(s):  
Support Vector Regression ◽  
Driving Behavior ◽  
Support Vector ◽  
Trajectory Data ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model ◽  
Importance Analysis ◽  
Model Training ◽  
Vehicle Trajectory

This paper presents a Support Vector Regression (SVR) approach that can be applied to predict the multianticipative driving behavior using vehicle trajectory data. Building upon the SVR approach, a multianticipative car-following model is developed and enhanced in learning speed and predication accuracy. The model training and validation are conducted by using the field trajectory data extracted from the Next Generation Simulation (NGSIM) project. During the model training and validation tests, the estimation results show that the SVR model performs as well as IDM model with respect to the model prediction accuracy. In addition, this paper performs a relative importance analysis to quantify the multianticipation in terms of the different stimuli to which drivers react in platoon car following. The analysis results confirm that drivers respond to the behavior of not only the immediate leading vehicle in front but also the second, third, and even fourth leading vehicles. Specifically, in congested traffic conditions, drivers are observed to be more sensitive to the relative speed than to the gap. These findings provide insight into multianticipative driving behavior and illustrate the necessity of taking into account multianticipative car-following model in microscopic traffic simulation.

Developing a Crash Warning System for the Bike Lane Area at Intersections with Connected Vehicle Technology

2019 ◽  
Vol 2673 (4) ◽  
pp. 47-58 ◽  
Author(s):  
Yina Wu ◽  
Mohamed Abdel-Aty ◽  
Ou Zheng ◽  
Qing Cai ◽  
Lishengsa Yue
Keyword(s):  
Traffic Safety ◽  
Warning System ◽  
Threshold Value ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Trajectory Data ◽  
Vehicle Trajectories ◽  
Connected Vehicle Technology ◽  
Turning Maneuver ◽  
The Right

A common type of bike lane at intersections is between the through lane and the right lane. With such design, right-turning drivers need to cross the bike lane to merge into the right lane, which could cause conflicts with bicycles on the keyhole bike lane. This study aims to develop a warning system for drivers to avoid vehicle–bicycle crashes in the bike lane area under a connected vehicle environment. To propose a reasonable warning system, 118 right-turning vehicle trajectories were collected by an unmanned aerial vehicle. Drivers’ right-turning behaviors are investigated based on the trajectory data. Then, a vehicle–bicycle crash warning algorithm is proposed to calculate the post-encroachment time (PET) under different situations. By comparing the threshold value and the PET value, potential vehicle–bicycle crash locations in the bike lane area could be identified. The proposed algorithm is designed to be displayed on front windshields with an augmented reality display. The results suggested that the proposed algorithm could provide high prediction accuracy. Moreover, vehicle speed, vehicle location, bicycle speed, and bicycle location were found to have significant impact on the locations of dangerous areas. It is expected that the proposed warning system could be used to identify the dangerous areas and deliver warning information for right-turning drivers when they are approaching an intersection. The warning system could help drivers be more prepared for the upcoming right-turning maneuver, and thus improve traffic safety for both drivers and cyclists at intersections.

scholarly journals Risky Driving Behavior Recognition Based on Vehicle Trajectory

2021 ◽  
Vol 18 (23) ◽  
pp. 12373
Author(s):  
Shengdi Chen ◽  
Qingwen Xue ◽  
Xiaochen Zhao ◽  
Yingying Xing ◽  
Jian John Lu
Keyword(s):  
Risk Evaluation ◽  
Evaluation Model ◽  
Driving Behavior ◽  
Trajectory Data ◽  
Behavior Recognition ◽  
Risky Driving ◽  
Car Following ◽  
Proposed Model ◽  
Behavior Based ◽  
Risk Evaluation Model

This paper proposes a measurement of risk (MOR) method to recognize risky driving behavior based on the trajectory data extracted from surveillance videos. Three types of risky driving behavior are studied in this paper, i.e., speed-unstable driving, serpentine driving, and risky car-following driving. The risky driving behavior recognition model contains an MOR-based risk evaluation model and an MOR threshold selection method. An MOR-based risk evaluation model is established for three types of risky driving behavior based on driving features to quantify collision risk. Then, we propose two methods, i.e., the distribution-based method and the boxplot-based method, to determine the threshold value of the MOR to recognize risky driving behavior. Finally, the trajectory data extracted from UAV videos are used to validate the proposed model. The impact of vehicle types is also taken into consideration in the model. The results show that there are significant differences between threshold values for cars and heavy trucks when performing speed-unstable driving and risky car-following driving. In addition, the difference between the proportion of recognized risky driving behavior in the testing dataset compared with that in the training dataset is limited to less than 3.5%. The recognition accuracy of risky driving behavior with the boxplot- and distribution-based methods are, respectively, 91% and 86%, indicating the validation of the proposed model. The proposed model can be widely applied to risky driving behavior recognition in video-based surveillance systems.

Understanding the Highway Safety Benefits of Different Approaches of Connected Vehicles in Reduced Visibility Conditions

2018 ◽  
Vol 2672 (19) ◽  
pp. 91-101 ◽  
Author(s):  
Md Sharikur Rahman ◽  
Mohamed Abdel-Aty ◽  
Ling Wang ◽  
Jaeyoung Lee
Keyword(s):  
Standard Deviation ◽  
Traffic Safety ◽  
Risk Index ◽  
Flow Characteristics ◽  
Crash Risk ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Car Following ◽  
Real Time Traffic ◽  
Surrogate Measures

This study evaluated the effectiveness of connected vehicle (CV) technologies in adverse visibility conditions using microscopic traffic simulation. Traffic flow characteristics deteriorate significantly in reduced visibility conditions resulting in high crash risks. This study applied CV technologies on a segment of Interstate I-4 in Florida to improve traffic safety under fog conditions. Two types of CV approaches (i.e., connected vehicles without platooning (CVWPL) and connected vehicles with platooning (CVPL) were applied to reduce the crash risk in terms of three surrogate measures of safety: the standard deviation of speed, the standard deviation of headway, and rear-end crash risk index (RCRI). This study implemented vehicle-to-vehicle (V2V) communication technologies of CVs to acquire real-time traffic data using the microsimulation software VISSIM. A car-following model for both CV approaches was used with an assumption that the CVs would follow this car-following behavior in fog conditions. The model performances were evaluated under different CV market penetration rates (MPRs). The results showed that both CV approaches improved safety significantly in fog conditions as MPRs increase. To be more specific, the minimum MPR should be 30% to provide significant safety benefits in terms of surrogate measures of safety for both CV approaches over the base scenario (non-CV scenario). In terms of surrogate safety measures, CVPL significantly outperformed CVWPL when MPRs were equal to or higher than 50%. The results also indicated a significant improvement in the traffic operation characteristics in terms of average speed.

scholarly journals Transferability of Car-Following Models Between Driving Simulator and Field Traffic

2017 ◽  
Vol 2623 (1) ◽  
pp. 60-72 ◽  
Author(s):  
Stavros Papadimitriou ◽  
Charisma F. Choudhury
Keyword(s):  
Driving Simulator ◽  
Statistical Tests ◽  
Real Life ◽  
Likelihood Estimation ◽  
The United States ◽  
Driving Behavior ◽  
Real Field ◽  
Trajectory Data ◽  
Car Following ◽  
Stimulus Response

During the past few decades, there have been two parallel streams of driving behavior research: models using trajectory data collected from the field (using video recordings, GPS, etc.) and models using data from driving simulators (in which the behavior of the drivers is recorded in controlled laboratory conditions). Although the former source of data is more realistic, it lacks information about the driver and is typically not suitable for testing effects of future vehicle technologies and traffic scenarios. In contrast, driving behavior models developed with driving simulator data may lack behavioral realism. However, no previous study has compared these two streams of mathematical models and investigated the transferability of the models developed with driving simulator data to real field conditions in a rigorous manner. The current study aimed to fill this research gap by investigating the transferability of two car-following models between a driving simulator and two comparable real-life traffic motorway scenarios, one from the United Kingdom and the other one from the United States. In this regard, stimulus–response–based car-following models were developed with three microscopic data sources: ( a) experimental data collected from the University of Leeds driving simulator, ( b) detailed trajectory data collected from UK Motorway 1, and ( c) detailed trajectory data collected from Interstate 80 in California. The parameters of these car-following models were estimated by using the maximum likelihood estimation technique, and the transferability of the models was investigated by using statistical tests of parameter equivalence and transferability test statistics. Estimation results indicate transferability at the model level but not fully at the parameter level for both pairs of scenarios.

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 Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities: A Review, Open Issues, and Future Perspectives

Telecom ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 108-140
Author(s):  
Paulo Álvares ◽  
Lion Silva ◽  
Naercio Magaia
Keyword(s):  
Traffic Congestion ◽  
Resource Constraints ◽  
Smart Cities ◽  
The Internet ◽  
Connected Vehicle ◽  
Sensitive Data ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Security Properties ◽  
Vehicle Networks

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

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