2018
DOI: 10.1111/risa.13217
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Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

Abstract: The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities. The evolving risk landscape will force a departure from traditional risk assessment approaches that rely on h… Show more

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Cited by 20 publications
(10 citation statements)
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“…In the last 5 years, the development of artificial intelligence (AI) has leapfrogged forward across several industries, ranging from quality control in various manufacturing areas to improved automation of production processes and enhanced diagnostics in medical applications. 1 Examples include voice and facial recognition, 2,3 landmark localization, 4 autonomic driving, [5][6][7][8][9] and a wide array of medical imaging modalities. [10][11][12][13][14][15][16][17][18][19][20][21] From a clinical standpoint, demonstrating the application of AI and its deep neural network learning algorithms is highly relevant and timely due to the ongoing debate on the necessity of advanced medical and surgical intervention while costs are rising.…”
Section: Introductionmentioning
confidence: 99%
“…In the last 5 years, the development of artificial intelligence (AI) has leapfrogged forward across several industries, ranging from quality control in various manufacturing areas to improved automation of production processes and enhanced diagnostics in medical applications. 1 Examples include voice and facial recognition, 2,3 landmark localization, 4 autonomic driving, [5][6][7][8][9] and a wide array of medical imaging modalities. [10][11][12][13][14][15][16][17][18][19][20][21] From a clinical standpoint, demonstrating the application of AI and its deep neural network learning algorithms is highly relevant and timely due to the ongoing debate on the necessity of advanced medical and surgical intervention while costs are rising.…”
Section: Introductionmentioning
confidence: 99%
“…Although these systems have shown great potential, successful future warning of food safety risk has been difficult to demonstrate. Recently, the usefulness of anomaly detection to identify risks was demonstrated in various studies (Li, et al, 2016;Ryan, et al, 2019;Salehi & Rashidi, 2018). Anomaly detection can not only detect the outliers of sample sets but also recognize rare events in nature as preliminary signals of risks (Rembold, et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…However, exploring alternative modalities, including event camera DMS (E-DMS) solutions, is both significant and timely given that such systems are expected to become mandatory across Europe by 2022. Moreover, as we progress to higher levels of vehicle automation, understanding the drivers cognitive and physical capabilities will be increasingly more important as control alternates between human and machine (Ryan et al, 2019(Ryan et al, , 2020Sheehan et al, 2017). Currently, there is a growing interest in the applicability of event cameras within the scope of autonomous vehicles (Binas et al, 2017;Chen, Cao, et al, 2020;Jianing Li et al, 2019;Maqueda et al, 2018;Nitti et al, 2020).…”
Section: Introductionmentioning
confidence: 99%