TrustVehicle – Improved Trustworthiness and Weather-Independence of Conditionally Automated Vehicles in Mixed Traffic Scenarios

2018 ◽  
pp. 75-89 ◽  
Author(s):  
Pamela Innerwinkler ◽  
Ahu Ece Hartavi Karci ◽  
Mikko Tarkiainen ◽  
Micaela Troglia ◽  
Emrah Kinav ◽  
...  
Keyword(s):  
Mixed Traffic ◽  
Automated Vehicles

Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles

2021 ◽  
Vol 152 ◽  
pp. 106006
Author(s):  
Iman Mahdinia ◽  
Amin Mohammadnazar ◽  
Ramin Arvin ◽  
Asad J. Khattak
Keyword(s):  
Mixed Traffic ◽  
Automated Vehicles

scholarly journals A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jiawen Wang ◽  
Shaobo Li ◽  
Yining Lu ◽  
Lubang Wang
Keyword(s):  
Cellular Automaton ◽  
Early Warning ◽  
Cellular Automaton Model ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Traffic Incidents ◽  
Lane Changing ◽  
Automatic Driving ◽  
Urban Expressway

Using a cellular automaton model, this paper studied the evolution mechanism of traffic incidents affecting the capacity of urban expressway under the mixed traffic environment of manual driving and automatic driving. It showed that the length of the automated-driving early-warning zone could affect the capacity of expressway. Specifically, the early-warning zone is divided into an accelerate lane-changing area, a decelerate lane-changing area, and a forced lane-changing area. The areas vary according to the distance between the vehicle and the location of incident. Based on the study, this paper establishes a codirectional two-lane cellular automaton model. The analysis showed that the capacity of the urban expressway varies under different combinations of early-warning area length and division ratio of early-warning zone. In the case of two-lane reduction caused by traffic incidents, the capacity of the expressway is optimized when the length of early-warning zone is between 450 and 600 m, and the ratio of accelerate zone, decelerate zone, and forced zone to the length of early-warning zone is, respectively, 75%, 10%, and 15%. In addition, this study showed that the capacity will rise with the increase in automated vehicles.

Cellular Automata–Based Modeling and Simulation of a Mixed Traffic Flow of Manual and Automated Vehicles

2017 ◽  
Vol 2622 (1) ◽  
pp. 105-116 ◽  
Author(s):  
Da Yang ◽  
Xiaoping Qiu ◽  
Lina Ma ◽  
Danhong Wu ◽  
Liling Zhu ◽  
...  
Keyword(s):  
Reaction Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Flow Characteristics ◽  
Lane Change ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Obvious Effect ◽  
Mixed Traffic Flow ◽  
Automated Vehicle

In recent years, automated vehicles have been developing rapidly, and some automated vehicles have begun to drive on highways. The market share of automated vehicles is expected to increase and will greatly affect traffic flow characteristics. This paper focuses on the mixed traffic flow of manual and automated vehicles. The study improves the existing cellular automaton model to capture the differences between manual vehicles and automated vehicles. Computer simulations are employed to analyze the characteristic variations in the mixed traffic flow under different automated vehicle proportions, lane change probabilities, and reaction times. Several new conclusions are drawn in the paper. First, with the increment of the proportion of automated vehicles, freeway capacity increases; the capacity increment is more significant for single-lane traffic than for two-lane traffic. Second, for single-lane traffic flow, reducing the reaction time of the automated vehicle can significantly improve road traffic capacity—as much as doubling it—and reaction time reduction has no obvious effect on the capacity of the two-lane traffic. Third, with the proportion increment of automated vehicles, lane change frequency reduces significantly. Fourth, when the density is 15 < ρ < 55 vehicles/km, the addition of 20% automated vehicles to a traffic flow that consisted of only manual vehicles can decrease congestion by up to 16.7%.

scholarly journals Will Automated Vehicles Negatively Impact Traffic Flow?

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
S. C. Calvert ◽  
W. J. Schakel ◽  
J. W. C. van Lint
Keyword(s):  
Traffic Flow ◽  
Gradual Transition ◽  
Mixed Traffic ◽  
Limited Data ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Low Level ◽  
Negative Effect ◽  
Vehicle Automation ◽  
Capacity Drop

With low-level vehicle automation already available, there is a necessity to estimate its effects on traffic flow, especially if these could be negative. A long gradual transition will occur from manual driving to automated driving, in which many yet unknown traffic flow dynamics will be present. These effects have the potential to increasingly aid or cripple current road networks. In this contribution, we investigate these effects using an empirically calibrated and validated simulation experiment, backed up with findings from literature. We found that low-level automated vehicles in mixed traffic will initially have a small negative effect on traffic flow and road capacities. The experiment further showed that any improvement in traffic flow will only be seen at penetration rates above 70%. Also, the capacity drop appeared to be slightly higher with the presence of low-level automated vehicles. The experiment further investigated the effect of bottleneck severity and truck shares on traffic flow. Improvements to current traffic models are recommended and should include a greater detail and understanding of driver-vehicle interaction, both in conventional and in mixed traffic flow. Further research into behavioural shifts in driving is also recommended due to limited data and knowledge of these dynamics.

Modeling System Dynamics of Mixed Traffic with Partially Connected and Automated Vehicles*

2019 ◽  
Author(s):  
Lianhua An ◽  
Xianfeng Yang ◽  
Yiming Zhang ◽  
Zihan Zhang ◽  
Jia Hu ◽  
...  
Keyword(s):  
System Dynamics ◽  
Mixed Traffic ◽  
Automated Vehicles
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