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    A Review of LiDAR-Based 3D Object Detection via Deep Learning Approaches Towards Robust Connected and Autonomous Vehicles
    (2025-01-01)
    Aung, Nang Htet Htet
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    Sangwongngam, Paramin
    ;
    Jintamethasawat, Rungroj
    ;
    Shah, Shashi
    ;
    Wuttisittikulkij, Lunchakorn
    Automated Driving Systems (ADS) rely on a variety of sensors and algorithms to perceive their environment and make proper and timely driving decisions to ensure safety and efficiency. One of their critical tasks is object detection, which detects and classifies object presence with a specific emphasis on cars, pedestrians, and cyclists, in 3D space in their surroundings with the use of sensing data from those sensors, including LiDAR. 3D object detection in Autonomous Vehicles (AVs) is a challenging problem due to the complexity and variability of real-world scenarios, such as occlusions, varying lighting conditions, and diverse object shapes and sizes. Moreover, the limitations of computing power and the availability of information about the surrounding environment, combined with the quality and age of sensors installed in standalone AVs, often lead to poor perception performance. In this context, Connected and Autonomous Vehicles (CAVs) have become a promising solution that can be employed to harness connectivity and external information and enhance their perception capabilities. In this paper, we provide a review of existing 3D object detection techniques based on deep learning for ADS using data from LiDAR and other sensors, as well as the architecture of CAVs and their communications, which facilitate heterogeneous networks of mobile and satellite communication technologies. We also discuss real-world and synthetic datasets for both single-vehicle and multi-view vehicles, such as vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X). Finally, we review the applications and future outlook of 3D object detection.
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    Evaluating the Determinants of Consumer Adoption of Autonomous Vehicles in Thailand—An Extended UTAUT Model
    (2023-01-01)
    Chaveesuk, Singha
    ;
    Chaiyasoonthorn, Wornchanok
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    Kamales, Nayika
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    Dacko-Pikiewicz, Zdzislawa
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    Liszewski, Wiesław
    This study explores the effects of autonomous cars and how they may affect the design of transportation systems. The research investigated the determinants of consumer adoption of autonomous vehicles in Thailand. The research was driven by increasing environmental protection awareness and the need to conserve it through revolutionary technology. The study adopted the extended UTAUT model, where a quantitative method was adopted using primary data from 381 respondents. The results indicated that consumer adoption of autonomous vehicles in Thailand is influenced by performance expectancy, effort expectancy, facilitating conditions, environmental benefits, and purchase subsidy. The recommendations developed were that, to enhance the consumers’ intention to adopt autonomous vehicles, the concerned stakeholders should improve on aspects, such as the ability to improve job performance, increase productivity, ease of use, flexibility, clarity, and understanding, as well as improve social status. The government should also consider subsidizing autonomous vehicles as this would encourage consumption. A limitation of the study is the generalization of the findings as it is limited to Thailand.