Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

Sarwar, Sohail; Zia, Saad; Qayyum, Zia Ul; Iqbal, Muddesar; Safyan, Muhammad; Mumtaz, Shahid; García‐Castro, Raúl; Igorevich, Kostromitin Konstantin

doi:10.1002/ett.3729

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…Driver Model Combined approaches for managing driving related information by a model representing the driver, the vehicle, and the context are described in [43,24]. An ontology for modeling driver profiles based on their demographic and behavioral aspects is proposed by [89]. It is used as an input for a hybrid learning approach for categorizing drivers according to their driving styles.…”

Section: Knowledge Representation Learningmentioning

confidence: 99%

A Survey on Knowledge Graph-based Methods for Automated Driving

Luettin¹,

Monka²,

Henson³

et al. 2022

Preprint

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Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD) in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all available information and knowledge in the driving task. Knowledge graphs (KG) have recently gained significant attention from both industry and academia for applications that benefit by exploiting structured, dynamic, and relational data. The complexity of graph-structured data with complex relationships and inter-dependencies between objects has posed significant challenges to existing machine learning algorithms. However, recent progress in knowledge graph embeddings and graph neural networks allows to applying machine learning to graph-structured data. Therefore, we motivate and discuss the potential benefit of KGs applied to the main tasks of AD including 1) ontologies 2) perception, 3) scene understanding, 4) motion planning, and 5) validation. Then, we survey, analyze and categorize ontologies and KG-based approaches for AD. We discuss current research challenges and propose promising future research directions for KG-based solutions for AD.

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Section: Knowledge Representation Learningmentioning

confidence: 99%

A Survey on Knowledge Graph-based Methods for Automated Driving

Luettin¹,

Monka²,

Henson³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It leverages the correlation between traffic demand and the number of parked vehicles. The proposed scheme assumes the existence of edge servers that control the activation states of CRUs because there are various types of drivers [20]. The contribution of this work is to propose a promising approach for efficiently deploy numerous small cells of mobile networks in 5G and beyond 5G era.…”

Section: Related Workmentioning

confidence: 99%

Small Cells Enabled by Crowdsourced Radio Units Mounted on Parked Vehicles for Smart City

et al. 2020

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A large number of small cells are densely deployed in the era of 5G and beyond 5G mobile networks to achieve high rate and low latency data transmission. Small cells are composed with fixed ground radio units (RUs) in the conventional networks, and thus the utilization rate of them is drastically deteriorated due to the spatio-temporal patterns of mobile traffic demand. To address this problem, adaptive network architectures have been investigated to reconfigure networks on demand using small cells mounted on moving objects such as vehicles and drones. However, it has been difficult to establish stable communication link to ground nodes in high-speed environments with these schemes. Therefore, this paper proposes a concept of small cells composed with crowdsourced RUs mounted on parked vehicles. They are activated based on the traffic demand in surrounding area by road side units (RSUs) assuming a smart city. The proposed idea is based on the correlation between the distribution of people and the occupancy rate of parking lots. It can efficiently improve user throughput with relatively small number of RUs. The feasibility of the proposed scheme is validated through numerical analysis, computer simulations, and experimental results.

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“…2) Time taken by Variants execution: Eight different algorithms of VT modules were acquired from Github [13] and other sources [14,15] as variants. Then every algorithm was run 5 times to sort an array of 10 thousand randomly generated element.…”

Section: F Exclusive Features Of Dpa-sftmentioning

confidence: 99%

An Adaptive Software Fault-Tolerant Framework for Ubiquitous Vehicular Technologies

Rizwan¹,

Nadeem²,

Iqbal³

et al. 2020

IEEE Comm. Stand. Mag.

Self Cite

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Probability for the occurrence of faults increases manifolds when program Lines of Code (LoC) exceeds a few thousand in ubiquitous applications. Faults mitigation in ubiquitous applications, such as those of autonomous Vehicular Technologies (VTs), has not been effective even with the use of formal methods. Faults in such applications require exhaustive testing for a timely fix, that seems infeasible computationally. This emphasizes the imperative role of Software Fault Tolerance (SFT) for autonomous applications. Several SFT techniques have been proposed but failures revealed in VT applications imply that existing SFT techniques need to be fine-tuned. In this paper, current replication-based SFT techniques have been analyzed and classified with respect to their diversity, adjudication, and adaptivity. Essential parameters (such as Reliability, Time, Variance, etc) for adjudication, diversity, and adaptiveness were recorded. The identified parameters were mapped to different techniques (such as AFTRC, SCOP, VFT, etc) for observing their shortcomings. Consequently, a generic framework named "Diverse Parallel Adjudication for Software Fault Tolerance (DPA-SFT)" has been proposed. DPA-SFT addresses the shortcomings of existing SFT techniques for VTs with the added value of parallel and diverse adjudication. A prototype implementation of the proposed framework has been developed for assessing the viability of DPA-SFT over modules of VT. An empirical comparison of the proposed framework was performed with prevalent techniques (AFTRC, SCOP, VFT, etc). A thorough evaluation suggests that DPA-SFT performs better than contemporary SFT techniques in VTs due to its parallel and diverse adjudication.

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Context aware ontology‐based hybrid intelligent framework for vehicle driver categorization

Cited by 10 publications

References 31 publications

A Survey on Knowledge Graph-based Methods for Automated Driving

A Survey on Knowledge Graph-based Methods for Automated Driving

Small Cells Enabled by Crowdsourced Radio Units Mounted on Parked Vehicles for Smart City

An Adaptive Software Fault-Tolerant Framework for Ubiquitous Vehicular Technologies

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