Analysis of cooperative driving strategies at road network level with macroscopic fundamental diagram

Zhang, Jiawei; Pei, Huaxin; Ban, Xuegang; Li, Li

doi:10.1016/j.trc.2021.103503

Cited by 26 publications

(3 citation statements)

References 84 publications

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“…We conduct experiments with the simulator presented in Refs. [33] and [34]. A pure tracking algorithm is used as the lateral control of the vehicle, referenced in Ref.…”

Section: Simulationmentioning

confidence: 99%

Autonomous Vehicles Testing Considering Utility-Based Operable Tasks

Zhang

et al. 2023

Tsinghua Sci. Technol.

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Virtual simulation testing of Autonomous Vehicles (AVs) is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs. Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets. However, the criticalities defined in their testing tasks are based on fixed assumptions, the obtained scenarios cannot pose a challenge to AVs with different strategies. To fill this gap, we propose an intelligent testing method based on operable testing tasks. We found that the driving behavior of Surrounding Vehicles (SVs) has a critical impact on AV, which can be used to adjust the testing task difficulty to find more challenging scenarios. To model different driving behaviors, we utilize behavioral utility functions with binary driving strategies. Further, we construct a vehicle interaction model, based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty. Finally, by adjusting SV's strategies, we can generate more corner cases when testing different AVs in a finite number of simulations.

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“…We conduct experiments with the simulator presented in Refs. [33] and [34]. A pure tracking algorithm is used as the lateral control of the vehicle, referenced in Ref.…”

Section: Simulationmentioning

confidence: 99%

Autonomous Vehicles Testing Considering Utility-Based Operable Tasks

Zhang

et al. 2023

Tsinghua Sci. Technol.

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“…Generally, cooperative driving planning can be roughly categorized into two kinds, namely, studies of non-idealized traffic scenarios [8][9][10][11] and studies of idealized traffic scenarios. In the first kind of study, researchers assume that the vehicles' lengths, arrivals, driving directions, and speeds are all random and timevarying [12][13][14] . Under such assumptions, we need to design adaptive and intelligent planning algorithms to schedule a short-term feasible passing order for the investigated vehicles, so that their delay can be minimized.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Li,

Zhang,

Chen

et al. 2024

Tsinghua Sci. Technol.

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Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion. In recent years, several cooperative driving approaches for idealized traffic scenarios (i.e., uniform vehicle arrivals, lengths, and speeds) have been proposed. However, theoretical analyses and comparisons of these approaches are lacking. In this study, we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance. We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles.The obtained conclusions shed light on future cooperative driving studies.

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“…In this paper, we consider the possible influence of recently emerging automated vehicles to traffic breakdown probability [ 18 , 19 ]. Usually, researchers find that shorter car-following gaps between automated vehicles at the microscopic level could directly lead to larger capacity and lower breakdown probability at the macroscopic level.…”

Section: Introductionmentioning

confidence: 99%

Traffic Breakdown Probability Estimation for Mixed Flow of Autonomous Vehicles and Human Driven Vehicles

Wei

Zhang

et al. 2023

Sensors

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Automated vehicles are expected to greatly boost traffic efficiency. However, how to estimate traffic breakdown probability for the mixed flow of autonomous vehicles and human driven vehicles around ramping areas remains to be answered. In this paper, we propose a stochastic temporal queueing model to reliably depict the queue dynamics of mixed traffic flow at ramping bottlenecks. The new model is a specified Newell’s car-following model that allows two kinds of vehicle velocities and first-in-first-out (FIFO) queueing behaviors. The jam queue join time is supposed to be a random variable for human driven vehicles but a constant for automated vehicles. Different from many known models, we check the occurrence of significant velocity drop along the road instead of examining the duration of the simulated jam queue so as to avoid drawing the wrong conclusions of traffic breakdown. Monte Carlo simulation results show that the generated breakdown probability curves for pure human driven vehicles agree well with empirical observations. Having noticed that various driving strategy of automated vehicles exist, we carry out further analysis to show that the chosen car-following strategy of automated vehicles characterizes the breakdown probabilities. Further tests indicate that when the penetration rate of automated vehicles is larger than 20%, the traffic breakdown probability curve of the mixed traffic will be noticeably shifted rightward, if an appropriate car-following strategy is applied. This indicates the potential benefit of automated vehicles in improving traffic efficiency.

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Analysis of cooperative driving strategies at road network level with macroscopic fundamental diagram

Cited by 26 publications

References 84 publications

Autonomous Vehicles Testing Considering Utility-Based Operable Tasks

Autonomous Vehicles Testing Considering Utility-Based Operable Tasks

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Traffic Breakdown Probability Estimation for Mixed Flow of Autonomous Vehicles and Human Driven Vehicles

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