A Survey on Datasets for the Decision Making of Autonomous Vehicles

Wang, Yuning; Han, Zeyu; Xing, Yining; Xu, Shaobing; Wang, Jianqiang

doi:10.1109/mits.2023.3341952

IEEE Intell. Transport. Syst. Mag.

2024

DOI: 10.1109/mits.2023.3341952

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A Survey on Datasets for the Decision Making of Autonomous Vehicles

Yuning Wang,

Zeyu Han,

Yining Xing

et al.

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2024

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Cited by 9 publications

References 72 publications

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A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

Alam,

Gupta,

Ahamed

et al. 2024

Neural Comput & Applic

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A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

Alam,

Gupta,

Ahamed

et al. 2024

Neural Comput & Applic

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Highly Interactive Self-Supervised Learning for Multi-Modal Trajectory Prediction

Xie,

Liu,

Zhao

et al. 2024

IFAC-PapersOnLine

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The Future of Transportation Design: Balancing Aesthetics and Functionality in Autonomous Vehicles

Praveena,

Manjunatha,

Dutt

et al. 2024

E3S Web Conf.

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This study examines the rapidly changing and competitive area of automobile design, with a particular focus on the introduction of autonomous vehicles. The study moves the focus of the conversation from conventional automotive design approaches to new, usercentric techniques which make use of the opportunities of autonomous technology. Moreover, it assesses the potential effects of different design choices on the experience of users, protection, and transportation within cities. Also, the research offers realistic viewpoints on the coming shifts and alterations in mobility design, predicting the significant effect of autonomous vehicles on both personal and social transportation models. When it comes to aesthetics, the recommendations for designing autonomous vehicles highlight finding an equilibrium between practicality and efficacy without compromising aesthetic appeal. Incorporating adaptable design characteristics that may be changed according to different user needs is advised, while also giving importance to ergonomics and simple designs that improve user interaction. Vehicle aerodynamics should be enhanced, and sustainable materials should be used if possible because these actions can help cut reduce emissions and energy use. In addition, the design process must take into account the integration of cutting-edge technology, including artificial intelligence and machine learning, to improve the vehicle's functionalities while verifying that aesthetic improvements have no impact on the vehicle's efficiency.

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A Survey on Datasets for the Decision Making of Autonomous Vehicles

Cited by 9 publications

References 72 publications

A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

Highly Interactive Self-Supervised Learning for Multi-Modal Trajectory Prediction

The Future of Transportation Design: Balancing Aesthetics and Functionality in Autonomous Vehicles

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