Reducing the risk of rear-end collisions with infrastructure-to-vehicle (I2V) integration of variable speed limit control and adaptive cruise control system

Li, Ye; Wang, Hao; Wang, Weixu; Liu, Shan-Wen; Xiang, Yun

doi:10.1080/15389588.2015.1121384

Cited by 73 publications

(33 citation statements)

References 27 publications

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“…Rear-end collision risk indexes (RCRI) establish relation between longitudinal safety and vehicle dynamic trajectory data. Previously, various indexes have been proposed and extensively applied [15], [46]. In this study, we utilize a RCRI based on safe stopping distance, which has been widely used in previous researches [4], [11], [20], [47], [48].…”

Section: B Surrogate Safety Measurementioning

confidence: 99%

Impact Evaluation of Cyber-Attacks on Traffic Flow of Connected and Automated Vehicles

Dong

Wang

et al. 2020

IEEE Access

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Connected and automated vehicles (CAVs) can improve transportation safety and efficiency based on vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications. However, there are potential cyber threats to the communication systems via on-board unit equipped in CAVs and roadside unit. Based on existing traffic flow models, we design an evaluation framework for cyber-attacks on CAVs, and further investigate the impact of proportion of cyber-attacked vehicles, cyber-attack severity, cyber-attack range and traffic demand. Moreover, performance of the transportation system is analysed based on four indicators, including efficiency, safety, emissions and fuel consumption. The numerical simulation results show that with the increase of cyber-attacked vehicles and higher cyber-attack severity, the negative impact on traffic flow gradually becomes notable with lower capacity, higher risk of rear-end collision, more air pollutants and fuel consumption. In addition, it may lead to accidents and inefficient traffic operations if cyber-attacks occur on position rather than speed, and thus the position-attacked traffic system consumes more energy and emits more pollutants. The findings of this study provide useful information for the prediction of future cyber-attacked traffic, comprehensive evaluation of transportation systems, as well as management of automated highway systems from the perspective of network security. INDEX TERMS Cyber-attack, cooperative adaptive cruise control, connected and automated vehicles, safety level.

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Section: B Surrogate Safety Measurementioning

confidence: 99%

Impact Evaluation of Cyber-Attacks on Traffic Flow of Connected and Automated Vehicles

Dong

Wang

et al. 2020

IEEE Access

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“…Among these measurements, TCC, firstly proposed by Hayward, (1972), is one of the most commonly utilized indicators to evaluate rear-end crash risks [40][41][42][43][44][45][46]. According to the definition of TCC [37], it refers to the time that remains for the occurrence of a collision between the two successive vehicles in the same lane.…”

Section: Surrogate Safety Measuresmentioning

confidence: 99%

Safety Analysis of a Modified Cooperative Adaptive Cruise Control Algorithm Accounting for Communication Delay

Wang

Hua

et al. 2020

Sustainability

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Cooperative adaptive cruise control (CACC) is a promising technology to improve traffic efficiency and enhance road safety. In this paper, a modified CACC control model considering the communication time delay is proposed, which is used to investigate the longitudinal safety impacts of the communication time delay to the CACC platoon. Then, the communication time delay model is integrated into the CACC model to simulate the realistic information transfer process in the CACC platoon. Then a microscopic CACC platoon simulation is designed and conducted to verify the feasibility and reliability of the modified CACC control algorithm. The obtained results reveal that the modified CACC control algorithm can not only reduce about 96.6% of inter-vehicle spacing error, but also enhance the vehicles’ ability to sense the upstream traffic changes. Furthermore, to quantitatively analyze the longitudinal safety influence of the time delay caused by representative communication systems, sensitivity analysis experiments of headway time were designed and conducted. In the sensitivity analysis, the time exposed time-to-collision (TET) and the time-integrated time-to-collision (TIT) were introduced as the key performance indicators (KPIs) to quantify the rear-end collision risks. Sensitivity analysis results demonstrate that the performance of the CACC platoon is strictly related to the applied wireless communication style. Furthermore, the CACC system supported by the 5th generation (5G) communication system shows great advantages in narrowing the minimal headway time gap and reducing the rear-end collision risks.

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“…With the development of information, communication and traffic technology, the vehicle-infrastructure cooperative system (VICS) has created a new generation of road traffic systems to meet the strong demand for traffic safety and optimized traffic flows. [1][2][3] The purpose of this system is to provide drivers with full-time spatial information through communication between on-board equipment and roadside equipment system and surrounding on-board equipment, so as to achieve maximum coordination between vehicles and roads and to enhance road traffic safety, patency, and efficiency to a greater extent. The IntelliDrive project launched by the U.S. Department of Transportation in 2009 is the predecessor of VICS, which provides vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications.…”

Section: Introductionmentioning

confidence: 99%

Traffic wave model based on vehicle‐infrastructure cooperative and vehicle communication data

Yuan

Zhang

Jiao

2020

Computational Intelligence

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Urban trunk road system undertakes the main traffic trip, and congestion occurs frequently in rush hours. In order to clearly describe the propagation process of traffic waves in signalized intersections, and then optimize phase difference. This article proposes a kinematic model for the traffic wave based on the physical mechanism of car-following and the kinematic characteristics of the traffic wave propagation. The actual road traffic monitoring data was extracted from the vehicle-infrastructure cooperative system and vehicle internal communication system. Then we obtained the values of the stop-and-start wave velocity. Compared with the measured data, the results showed that the calculation of the wave velocity of the traffic wave model had a relative error of up to 5% vs the measured data, confirming the validity of the model. Through the analysis of the model, we obtained the difference in the effects on traffic wave velocity of the vehicle speed and the space headway. Our findings provide a theoretical basis for coordinated control and management of urban trunk road traffic and phase difference optimization of signalized intersections. Meanwhile, the research results

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Reducing the risk of rear-end collisions with infrastructure-to-vehicle (I2V) integration of variable speed limit control and adaptive cruise control system

Abstract: Compared to VSL-only and ACC-only scenarios, this integrated I2V system is more effective in reducing rear-end collision risks. The findings of this study provide useful information for traffic agencies to implement novel techniques to improve safety on freeways.

Cited by 73 publications

References 27 publications

Impact Evaluation of Cyber-Attacks on Traffic Flow of Connected and Automated Vehicles

Impact Evaluation of Cyber-Attacks on Traffic Flow of Connected and Automated Vehicles

Safety Analysis of a Modified Cooperative Adaptive Cruise Control Algorithm Accounting for Communication Delay

Traffic wave model based on vehicle‐infrastructure cooperative and vehicle communication data

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