F-cooper

Chen, Qi; Ma, Xu; Tang, Sihai; Guo, Jingda; Yang, Qing; Fu, Song

doi:10.1145/3318216.3363300

Cited by 195 publications

(9 citation statements)

References 36 publications

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“…The method could represent the road from offline point cloud data accurately. Chen et al [ 91 ] proposed a hybrid method for mering point cloud data based on two types of point cloud features obtained from voxel feature fusion and spatial feature fusion, respectively. Arnold et al [ 92 ] introduced a point cloud hybrid fusion method based on a 3D object detection network.…”

Section: Multi-sensor Data Fusion Methodsmentioning

confidence: 99%

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Wang,

Wu,

Liang

et al. 2024

Sensors

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Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle–infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle–infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

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Section: Multi-sensor Data Fusion Methodsmentioning

confidence: 99%

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Wang,

Wu,

Liang

et al. 2024

Sensors

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“…Third, Autonomous Driving is able to greatly improve traffic efficiency [68], [69] through analysis of a collection of relevant data. To achieve efficient autonomous driving, we need to collect information about traffic, weather, parking lots, date and time, etc.…”

Section: E Examplesmentioning

confidence: 99%

Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

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The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.INDEX TERMS Machine learning, Internet of Things, search engine, edge intelligence, applications.

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“…Unfortunately, detecting failures of AI components is not a trivial task and, indeed, is the subject of active research. To this end, relying on distributed AI systems [98] is one alternative to fac ing the challenges with the lack of comprehensive data to train AI/ML models as well as to provide redundancy during ADAS online operations [99]. In the former case, models are built relying on a federation of devices-coordinated by Edge or Cloud servers-that contribute to learning with their local data and specific knowledge [98,99].…”

Section: Ai Components In Adasmentioning

confidence: 99%

“…To this end, relying on distributed AI systems [98] is one alternative to fac ing the challenges with the lack of comprehensive data to train AI/ML models as well as to provide redundancy during ADAS online operations [99]. In the former case, models are built relying on a federation of devices-coordinated by Edge or Cloud servers-that contribute to learning with their local data and specific knowledge [98,99]. In the latter case, Edge or Cloud servers contribute to decision making during ADAS operations.…”

Section: Ai Components In Adasmentioning

confidence: 99%

On Driver Behavior Recognition for Increased Safety: A Roadmap

Davoli

Martalò

Cilfone

et al. 2020

Safety

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Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

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F-cooper

Cited by 195 publications

References 36 publications

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Towards Efficient and Intelligent Internet of Things Search Engine

On Driver Behavior Recognition for Increased Safety: A Roadmap

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