Future Role of Artificial Intelligence in Advancing Transportation Electrification

Lin, Hongyi; Yan, Yiping; Cheng, Qixiu

doi:10.26599/jicv.2023.9210020

Cited by 15 publications

(3 citation statements)

References 9 publications

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“…The emergence of electric buses, contactless payment systems, and real-time tracking technologies are examples of the progress made in modernizing public transport (Ongkittikul and Geerlings, 2006;Camacho et al, 2016;Bakker and Konings, 2018;Lusikka et al, 2020). Self-driving or autonomous vehicles (AVs) have the potential to revolutionize urban transport, improve safety, reduce congestion, and optimize traffic flow (Lin et al, 2023). With the advent of the Internet of Things (IoTs), cities are transitioning to intelligent transportation systems that utilize data analytics and real-time monitoring to manage traffic flow, alleviate congestion, and enhance overall transportation efficiency (Sumalee and Ho, 2018).…”

Section: Overview Of the Ground Transportation Systemmentioning

confidence: 99%

“…While much has been discussed regarding the implications of autonomous cars for overall mobility, it is important to recognize that many of the challenges and barriers they face can also be applied to flying vehicles (Qu et al, 2022b;Zeng and Qu, 2023). Key technologies, such as control algorithms, play a critical role in enabling not only autonomous flights but also complex maneuvers, such as formation flights and detect-and-avoid strategies (Wu and Qu, 2022;Lin et al, 2023;Liu et al, 2023b).…”

Section: Autonomous Ground Vehicles and Their Rolementioning

confidence: 99%

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Urban air mobility (UAM) and ground transportation integration: A survey

Yan,

Wang,

2024

Front. Eng. Manag.

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This study explores urban air mobility (UAM) as a strategy for mitigating escalating traffic congestion in major urban areas as a consequence of a static transportation supply versus dynamic demand growth. It offers an in-depth overview of UAM development, highlighting its present state and the challenges of integration with established urban transport systems. Key areas of focus include the technological advancements and obstacles in electric vertical take-off and landing (eVTOL) aircrafts, which are essential for UAM operation in urban environments. Furthermore, it explores the infrastructure requirements for UAM, including vertiport deployment and the creation of adept air traffic control (ATC) systems. These developments must be integrated into the urban landscape without exacerbating land-use challenges. This paper also examines the regulatory framework for UAM, including existing aviation regulations and the necessity for novel policies specifically designed for urban aerial transport. This study presents a comprehensive perspective for various stakeholders, from policymakers to urban planners, highlighting the need for a thorough understanding of UAM’s potential and effective assimilation into urban mobility frameworks.

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Section: Overview Of the Ground Transportation Systemmentioning

confidence: 99%

Section: Autonomous Ground Vehicles and Their Rolementioning

confidence: 99%

Urban air mobility (UAM) and ground transportation integration: A survey

Yan,

Wang,

2024

Front. Eng. Manag.

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“…They applied multi‐front vehicle feedback with integrated speed and acceleration to construct the CAV following model, thus effectively reducing the collision probability. Currently, the applications are assisted autonomous vehicles, and it is necessary to further improve the performance of advanced driver assistance systems (ADAS) to predict traffic safety risks in a data‐driven manner, using elastic network regularized multinomial logistic regression and autonomous driving data to construct prediction models [18, 19]. Li et al.…”

Section: Introductionmentioning

confidence: 99%

Safety analysis of autonomous vehicles based on target detection error

Rong,

Jin,

Liu

et al. 2024

IET Intelligent Trans Sys

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Connected and automated vehicles (CAVs) rely on their perception systems to detect traffic objects, with the uncertainty in detection results significantly influencing the safety of their decision‐making and control mechanisms. This paper introduces a safety potential field for CAVs that accounts for target detection errors. Initially, the paper categorizes errors arising from target detection into classification, labelling, and positioning categories. Subsequently, an elliptical model‐based safety potential field is developed, incorporating potential field line optimization using safety thresholds and lane lines. This approach facilitates the determination of critical values and safety distribution for the potential field. The paper then proceeds with coefficient calibration and experimental analysis to validate the reliability of the proposed model. Findings indicate that as target detection errors increasingly manifest, the safety potential field area for CAVs becomes more restrictive, enhancing the field's sensitivity to these errors. The critical safety value for CAVs is maintained within the range of [0 m, 7 m], providing a stable basis for decision‐making and control. Additionally, the safety value for CAVs falls between [15, 25], favouring the improvement of safety gradient distribution under the calibrated safety potential field values.

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