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

Chang, Xin; Li, Haijian; Rong, Jian; Huang, Z. Rena; Chen, Xiaoxuan; Zhang, Yunlong

doi:10.1155/2019/8591623

Cited by 37 publications

(16 citation statements)

References 23 publications

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“…Indeed, in (Ali et al, 2020), the authors have found that this approach of driver notification trough the connected environment improve the behavior of the driver in terms of safety when communications are well-established. In (Chang et al, 2019), the effect of an onboard unit (OBU) as a V2V technology has been analyzed in different traffic scenarios. Based on the analysis presented in their work, the authors found that the use of onboard units has a positive impact on drivers' driving behaviors.…”

Section: Related Workmentioning

confidence: 99%

Toward an Intelligent Driving Behavior Adjustment Based on Legal Personalized Policies Within the Context of Connected Vehicles

Outay

Jabeur

Haddad

et al. 2021

Front. Built Environ.

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The advent of Connected Vehicles (CVs) is creating new opportunities within the transportation sector. It is, indeed, expected to improve road traffic safety, enhance mobility, reduce fuel consumption and gas emissions, as well as foster economic growth via investments and jobs. However, to motivate the deployment of CVs and maximize their related benefits, policymakers must create appropriate neutral legal frameworks. These frameworks should promote the innovation of current road infrastructures, support cooperation and interoperability between transportation systems, and encourage fair competition between companies while upholding consumer privacy as well as data protection. We argue that policymakers should also support innovative mobility services toward a better accommodation of individual drivers and vehicles. Within this scope, we are proposing in this paper an intelligent approach that promotes the implementation of personalized road policies based on driving behaviors, driving performance, and the ongoing road traffic situation. These policies, which are dynamic in space and time, ultimately aim to increase drivers’ awareness by encouraging behavioral self-regulation. To meet our goals, we are using software agents that autonomously manage the driving behaviors according to well-defined transitions between driving states while enabling appropriate message exchanges between CVs. We run software simulations as well as field tests and obtained promising results that would reflect the relevance of implementing our vision of personalized policies.

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Section: Related Workmentioning

confidence: 99%

Toward an Intelligent Driving Behavior Adjustment Based on Legal Personalized Policies Within the Context of Connected Vehicles

Outay

Jabeur

Haddad

et al. 2021

Front. Built Environ.

View full text Add to dashboard Cite

show abstract

“…Intersection collisions account for over 40% of total traffic accidents, which not only seriously threaten people's lives, but also cause severe traffic congestion [2]. Research has shown that over 60% of these collisions can be avoided if drivers receive a warning just half a second in advance [3,4]. To improve traffic problems and build smart cities, the intelligent transportation system (ITS) has been widely studied [5,6].…”

Section: Introductionmentioning

confidence: 99%

Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Bai

Zhang

et al. 2021

Sensors

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Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.

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“…With the development of connected vehicles (CVs), research on tra c ow theory has gradually evolved from traditional tra c ow modeling to connected tra c ow [10][11][12][13][14][15][16]. A CV can share its status (e.g., position, speed, and acceleration) with other CVs within a certain range via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication.…”

Section: Introductionmentioning

confidence: 99%

Analysis of V2V Messages for Car-Following Behavior with the Traffic Jerk Effect

Hui

Liu

et al. 2020

Journal of Advanced Transportation

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The existing model of sudden acceleration changes, referred to as the traffic jerk effect, is mostly based on theoretical hypotheses, and previous research has mainly focused on traditional traffic flow. To this end, this paper investigates the change in the traffic jerk effect between inactive and active vehicle-to-vehicle (V2V) communications based on field experimental data. Data mining results show that the correlation between the jerk effect and the driving behavior increases by 50.6% on average when V2V messages are received. In light of the data analysis results, a new car-following model is proposed to explore the jerk effect in a connected environment. The model parameters are calibrated, and the results show that the standard deviation between the new model simulation data and the observed data decreases by 38.2% compared to that of the full velocity difference (FVD) model. Linear and nonlinear analyses of the calibrated model are then carried out to evaluate the connected traffic flow stability. Finally, the theoretical analysis is verified by simulation experiments. Both the theoretical and simulation results show that the headway amplitude and velocity fluctuations are reduced when considering the jerk effect in a connected environment, and the traffic flow stability is improved.

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

Cited by 37 publications

References 23 publications

Toward an Intelligent Driving Behavior Adjustment Based on Legal Personalized Policies Within the Context of Connected Vehicles

Toward an Intelligent Driving Behavior Adjustment Based on Legal Personalized Policies Within the Context of Connected Vehicles

Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Analysis of V2V Messages for Car-Following Behavior with the Traffic Jerk Effect

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