Modeling and field experiments on autonomous vehicle lane changing with surrounding human‐driven vehicles

Wang, Zhen; Zhao, Xiangmo; Xu, Zhijie; Li, Xiaopeng; Qu, Xiaobo

doi:10.1111/mice.12540

Cited by 40 publications

(12 citation statements)

References 52 publications

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“…Z. Wang, Zhao, et al (2021) developed a model for AVs' lanechanging process incorporating human driving behavior in a mixed-vehicular environment. Kim et al (2021) proposed a learning-based framework that can perform two tasks of prediction and planning recursively at each step and simultaneously make the prediction of the surrounding traffic flow and the planning of AV paths.…”

Section: Autonomous Trafficmentioning

confidence: 99%

Analysis on Braess paradox and network design considering parking in the autonomous vehicle environment

Zhang,

Waller,

Lin

2023

Computer aided Civil Eng

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This study is the first in the literature to examine the Braess paradox considering parking behavior in the autonomous vehicle (AV) environment, based on which the network design problem for the autonomous transportation system (NDP‐ATS) is modeled. First, we introduce the AV commuting pattern considering the self‐driving process for the parking purpose. We then illustrate the existence of two distinct Braess paradoxes that can occur in AV traffic networks with regards to parking space addition and network capacity expansion. They show that these two types of “improvement” measures might deteriorate the network system performance in an AV situation. Second, motivated to avoid the deterioration due to the introduced Braess paradoxes, we develop a bi‐level programming model for NDP‐ATS. The lower‐level program addresses the network‐equilibrium traffic assignment for AVs. The upper‐level program aims to minimize the network‐wide travel cost considering both occupied and empty AV trips by selecting the optimal design option accounting for road capacity enhancement and parking facility deployment simultaneously. To solve the developed bi‐level model, the simplicial homology global optimization algorithm is employed along with the Frank–Wolfe algorithm and the CPLEX. Finally, the efficacy of the modeling framework is validated through case studies on the Sioux Falls city network and the Anaheim network. The results show that the developed methodology is capable of producing high‐quality solutions with respect to savings in system‐level travel costs for AV traffic. The results also highlight the impacts of parking fees on the system performance for the optimized network. Additionally, it is found that the two objectives, which minimize the total system travel cost and minimize that for empty AV trips only, are conflicting for certain scenarios. The methodological approach introduced in this work serves as a critical modeling device for infrastructure development and policy assessment for AV traffic.

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Section: Autonomous Trafficmentioning

confidence: 99%

Analysis on Braess paradox and network design considering parking in the autonomous vehicle environment

Zhang,

Waller,

Lin

2023

Computer aided Civil Eng

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“…One Balser stereo camera was installed on both sides of the LiDAR, and one SICK millimeter wave radar was installed on both sides of the front of the vehicle. In addition, the AV was equipped with an industrial control computer (model ARK-3500), which had an i7 processor, 32 GB memory, 1 TB solid-state disk, 1050 Ti GPU, and a CAN bus interface card [22]. With such customization, the AV could be under both longitudinal and latitudinal control automatically for a complete route under normal conditions; thus, it can be classified as being at Level 3 automation.…”

Section: Test Vehiclementioning

confidence: 99%

Can Autonomous Vehicles Save Fuel? Findings from Field Experiments

Zhang

Peng

et al. 2022

Journal of Advanced Transportation

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The majority of the more recent studies have mainly focused on how to achieve energy-efficient goals by optimizing the driving behavior for human-driven vehicles or designing trajectory planning and tacking algorithms for autonomous vehicles. However, the energy-saving advantages of autonomous vehicles have not been quantitatively and theoretically explored. Therefore, this study aims to specifically clarify whether autonomous vehicles use less fuel than human-driven ones. First, the differences in driving behavior, regarding speed control, between autonomous vehicles and human-driven vehicles were compared. The most notable difference between them is that an autonomous vehicle can control the vehicle speed more effectively, with less speed fluctuations than a human driver. Subsequently, the influence of speed fluctuation on vehicle fuel consumption (L/100 km) was formulated based on the vehicle specific power (VSP) model. The mathematical deduction showed that the fuel consumption is proportional to the speed fluctuation under the same mean speed. Finally, simulation experiments were conducted under real scenarios. The simulation data showed that the fuel consumption increases almost linearly with the increase in speed fluctuation. Field experiments were also conducted on the fuel consumption of an autonomous vehicle under different driving modes. The experimental data showed that the fuel consumption also increases almost linearly with the increase in speed fluctuation. In the human-driven mode, the fuel consumption increased by 5.6% and 14.7%, respectively, compared with that in the autonomous mode at average speeds of 20 km/h and 40 km/h. Furthermore, the maximum fuel consumption was up to 60% more when the autonomous vehicle was driven by a driver, as the driving behavior displayed greater speed fluctuations.

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“…Hence, decision‐making algorithms adapted to the complex traffic environment will play a critical role. For autonomous vehicles, significant progress has been made, such as automatic parking (Zhang et al., 2021), trajectory tracking (Z. Wang et al., 2021), and risk assessment (Zheng et al., 2021). However, autonomous vehicles operate based only on their sensors and are hard to employ cooperatively.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous data‐based spatiotemporal trajectory synchronization for virtual–real interactive testing

Qiu

Shangguan

Cai

et al. 2022

Computer aided Civil Eng

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Virtual–real interactive testing (VRIT) is a practical approach to assessing the safety and intelligence of connected vehicles. Under this testing scheme, state consistency between physical and virtual spaces must be guaranteed. This paper proposes a multispace synchronization framework to ensure the desired interactive performance of VRIT systems. According to this framework, the synchronization process is decomposed into tracking and extensional periods. In addition, the heterogeneous data collected from physical and virtual spaces are divided into three layers. Each layer has a specific priority in the generation of the tracking trajectory. Furthermore, an extensional trajectory is integrated based on the proposed filtering mechanism, which improves the synchronization accuracy under high latency conditions. Finally, numerical experiments are conducted to demonstrate the superiority of the proposed method and to illustrate how the key parameters affect the system performance.

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Modeling and field experiments on autonomous vehicle lane changing with surrounding human‐driven vehicles

Cited by 40 publications

References 52 publications

Analysis on Braess paradox and network design considering parking in the autonomous vehicle environment

Analysis on Braess paradox and network design considering parking in the autonomous vehicle environment

Can Autonomous Vehicles Save Fuel? Findings from Field Experiments

Heterogeneous data‐based spatiotemporal trajectory synchronization for virtual–real interactive testing

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