Evaluating the impact of connected and autonomous vehicles on traffic safety

Ye, Lanhang; Yamamoto, Toshiyuki

doi:10.1016/j.physa.2019.04.245

Cited by 173 publications

(79 citation statements)

References 20 publications

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“…Qin et al [49] analyzed the differences in traffic behavior between cooperative adaptive cruise control (CACC)/ACC vehicles and manually driven vehicles and combined their respective advantages and disadvantages to build a car-following model and establish a theoretical basis for heterogeneous traffic flow. Ye et al [50][51][52][53] established a model for ICVs in mixed traffic flows based on a two-lane cellular automation model for heterogeneous traffic flows. Subsequently, they proposed a modeling method for ICVs based on the chart method and simulated the model considering factors such as density, speed, performance, and permeability.…”

Section: Research On Automatic-manual Mixed Traffic Flowmentioning

confidence: 99%

Research Progress and Prospects of Vehicle Driving Behavior Prediction

Zheng

2021

WEVJ

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Autonomous driving technology is vital for intelligent transportation systems. Vehicle driving behavior prediction is the foundation and core of autonomous driving. A detailed review of the existing research on vehicle driving behavior prediction can improve the understanding of the current progress of research on autonomous driving and provide references for follow-up researchers. This paper primarily reviews and analyzes the control models of autonomous driving, prejudgment methods, on-road and intersection traffic decision-making, and shortcomings of the research about the prediction of individual intelligent vehicle driving behavior, the prediction on movements of vehicles connected via the Internet, and prediction of driving behavior in a mixed traffic environment. The deficiencies in the research on vehicle driving behavior prediction are as follows: (1) there are numerous limitations in the intelligent application scenarios of individual intelligent vehicles; (2) although the Internet of Vehicles is a significant developmental trend, the training and test datasets are not rich enough; and (3) as the research of mixed traffic flow is still in the initial stages, the comfort brought by autonomous driving in hybrid driving environments is not being considered. In addition to the above analyses and comments, the future research prospects of vehicle driving behavior prediction are discussed as well.

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Section: Research On Automatic-manual Mixed Traffic Flowmentioning

confidence: 99%

Research Progress and Prospects of Vehicle Driving Behavior Prediction

Zheng

2021

WEVJ

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“…The new generation connected technology including the C-V2X as well as the ITS-G5 can transmit information between vehicles, facilities and other elements in the transportation system in a low-latency and high-reliability way [1][2][3][4][5][6][7][8][9], which makes it possible for drivers to obtain massive information about elements in the system in real time. A larger number of efforts were carried out to explore the impacts of the new connected technologies on the operation of the transportation system [10][11][12][13][14][15]. Among these researches, a considerable part of the important ones explore the impacts of the application of the new connected technologies on car-following behavior or traffic flow from the perspective of traffic flow theory, which is the theoretical basis for effectively organizing and managing the transportation system.…”

Section: Introductionmentioning

confidence: 99%

Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

et al. 2021

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With the application of vehicles to everything (V2X) technologies, drivers can obtain massive traffic information and adjust their car-following behavior according to the information. The macro-characteristics of traffic flow are essentially the overall expression of the micro-behavior of drivers. There are some shortcomings in the previous researches on traffic flow in the V2X environment, which result in difficulties to employ the related models or methods in exploring the characteristics of traffic flow affected by the information of generalized preceding vehicles (GPV). Aiming at this, a simulation framework based on the car-following model and the cellular automata (CA) is proposed in this work, then the traffic flow affected by the information of GPV is simulated and analyzed utilizing this framework. The research results suggest that the traffic flow, which is affected by the information of GPV in the V2X environment, would operate with a higher value of velocity, volume as well as jamming density and can maintain the free flow state with a much higher density of vehicles. The simulation framework constructed in this work can provide a reference for further research on the characteristics of traffic flow affected by various information in the V2X environment.

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“…Many experts and scholars have turned their attention to smart cars with the vigorous development of modern information and automotive technologies. Considering that smart cars can autonomously drive, they have become an important way to eliminate hidden dangers of traffic safety [ 5 , 6 ]. Vehicle detection is an indispensable part of environmental perception technology for smart cars.…”

Section: Introductionmentioning

confidence: 99%

“…(3) When M > 0 and the ratio of positive and negative samples is controlled to 1:3, the weighted mask for negative sample classification is set to neg_mask = {1 − label}/M × 3. (4)The weighted mask used for classification task is cls_mask = pos_mask + neg_mask(5) Assuming that the weight coefficient of regression task is α the weighted mask used for regression task is reg_mask = pos_mask × α.…”

mentioning

confidence: 99%

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

Cao

Song

et al. 2020

Sensors

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Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people’s lives and property.

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Evaluating the impact of connected and autonomous vehicles on traffic safety

Cited by 173 publications

References 20 publications

Research Progress and Prospects of Vehicle Driving Behavior Prediction

Research Progress and Prospects of Vehicle Driving Behavior Prediction

Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

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