Intelligent Detection of Driver Behavior Changes for Effective Coordination Between Autonomous and Human Driven Vehicles

Nallaperuma, Dinithi; Silva, Daswin De; Alahakoon, Damminda; Yu, Xinghuo

doi:10.1109/iecon.2018.8591357

Cited by 25 publications

(15 citation statements)

References 23 publications

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“…2). The underlying base algorithm has been applied to examine context awareness in the Aarhus city of Denmark motor traffic dataset [20] and to study how the driver behavior change can affect the coordination between autonomous and human-driven vehicles [21], and the algorithm has been extended in this work for recurrent and non-recurrent concept drift capture. The algorithm consists of two forms of learning, online and offline.…”

Section: B Unsupervised Concept Drift Detection Based On Online Incremental Machine Learningmentioning

confidence: 99%

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

Nallaperuma

Nawaratne

Bandaragoda

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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205

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The technological landscape of intelligent transport systems (ITS) has been radically transformed by the emergence of the big data streams generated by the Internet of Things (IoT), smart sensors, surveillance feeds, social media, as well as growing infrastructure needs. It is timely and pertinent that ITS harness the potential of an artificial intelligence (AI) to develop the big data-driven smart traffic management solutions for effective decision-making. The existing AI techniques that function in isolation exhibit clear limitations in developing a comprehensive platform due to the dynamicity of big data streams, highfrequency unlabeled data generation from the heterogeneous data sources, and volatility of traffic conditions. In this paper, we propose an expansive smart traffic management platform (STMP) based on the unsupervised online incremental machine learning, deep learning, and deep reinforcement learning to address these limitations. The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and impact propagation, traffic flow forecasting, commuter sentiment analysis, and optimized traffic control decisions. The platform is successfully demonstrated on 190 million records of smart sensor network traffic data generated by 545,851 commuters and corresponding social media data on the arterial road network of Victoria, Australia.

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Section: B Unsupervised Concept Drift Detection Based On Online Incremental Machine Learningmentioning

confidence: 99%

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

Nallaperuma

Nawaratne

Bandaragoda

et al. 2019

IEEE Trans. Intell. Transport. Syst.

Self Cite

205

View full text Add to dashboard Cite

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“…Based on the IKASL learning approach, this algorithm advances into decremental learning and online learning for continuous detection and adaption to concept drift from an unlabeled data stream. A variation of this technique was applied to explore the importance of context awareness to estimate road traffic [43], investigate the impact of driver behavior change on the coordination between self-driven and human-driven vehicles [44], and as the core machine learning function of an expansive intelligent traffic data integration and analysis platform [45]. The proposed algorithm consists Fig.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

Jayaratne

Silva

Alahakoon

et al. 2021

Discov Artif Intell

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The embedded, computational and cloud elements of industrial cyber physical systems (CPS) generate large volumes of data at high velocity to support the operations and functions of corresponding time-critical and mission-critical physical entities. Given the non-deterministic nature of these entities, the generated data streams are susceptible to dynamic and abrupt changes. Such changes, which are formally defined as concept drifts, leads to a decline in the accuracy and robustness of predicted CPS behaviors. Most existing work in concept drift detection are classifier dependent and require labeled data. However, CPS data streams are unlabeled, unstructured and change over time. In this paper, we propose an unsupervised machine learning algorithm for continuous concept drift detection in industrial CPS. This algorithm demonstrates three types of unsupervised learning, online, incremental and decremental. Furthermore, it distinguishes between abrupt and reoccurring drifts. We conducted experiments on SEA, a widely cited synthetic dataset of concept drift detection, and two industrial applications of CPS, task tracking in factory settings and smart energy consumption. The results of these experiments successfully validate the key features of the proposed algorithm and its utility of detecting change in non-deterministic CPS environments.

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“…Ainda no contexto de abordagem não-supervisionadas, encontrou-se os trabalhos (Kim et al, 2019;Nallaperuma et al, 2018). No trabalho de (Nallaperuma et al, 2018), os autores implementam um modelo com um algoritmo de aprendizado de máquina incremental não-supervisionado para detecção de mudança de comportamento abrupta e repetida.…”

Section: Trabalhos Relacionadosunclassified

Uma abordagem não supervisionada para modelagem e análise do comportamento de motoristas

Silva

Signoretti

Andrade

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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The growth of population and urban activities provides the emergence of problems in urban systems, such as congestion, increased traffic, car accidents, among others, affecting Urban Mobility. Thus, it is known that one of the main villains are vehicles and that traditional solutions are no longer effective in solving the problem, requiring technological solutions. Therefore, the objective of this work is to propose an approach to be supported by urban mobility in order to identify anomalies in the pavements, as well as to establish the driver's behavior, through unsupervised algorithms. In addition, a case study was carried out in the city of Natal. As a result, it was found that it is possible to identify how drivers travel on roads with anomalies such as uneven pavements, speed reducers, elevated pedestrian crossings and potholes. Resumo: O crescimento da população e das atividades urbanas proporciona o surgimento de problemas nos sistemas urbanos, como congestionamentos, aumento do tráfego, acidentes automobilísticos, entre outros, afetando a Mobilidade Urbana. Assim, sabe-se que um dos principais vilões são os veículos e que soluções tradicionais não são mais eficazes para solucionar o problema, necessitando de soluções tecnológicas. Diante disso, o objetivo desse trabalho é propor uma abordagem para ser suportada pela mobilidade urbana com o intuito de identificar anomalias nas pavimentações, bem como estabelecer o comportamento do motorista, por meio de algoritmos não supervisionados. Além disso, um estudo de caso foi realizado na cidade de Natal. Como resultado, foi verificado que é possível identificar como os motoristas trafegam sob vias com anomalias como pavimentos irregulares, redutores de velocidade, faixa de pedestres elevadas e buracos.

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Intelligent Detection of Driver Behavior Changes for Effective Coordination Between Autonomous and Human Driven Vehicles

Cited by 25 publications

References 23 publications

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

Uma abordagem não supervisionada para modelagem e análise do comportamento de motoristas

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