2019
DOI: 10.1109/tiv.2019.2919470
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A Novel Integrated Simulation and Testing Platform for Self-Driving Cars With Hardware in the Loop

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Cited by 78 publications
(27 citation statements)
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“…Nonetheless, FAGVs are being tested in realtime in a number of places without the necessity of a back-up driver such as Arizona, California, Michigan, and Ohio [13]. Effective testing and simulation environment is a vital part in the research of self-driving cars, which can test selfdriving software and hardware quickly in different virtual environments at low cost [33]. Ten officially designated autonomous vehicle test sites designed and supported by the US Department of Transportation, one of which is GoMentum Station in California with 20 miles of paved roads and a cluster of barracks and buildings in an urban environment, provide grounds to help set the direction of policy-making and testing procedures for FAGVs [34].…”
Section: Companymentioning
confidence: 99%
“…Nonetheless, FAGVs are being tested in realtime in a number of places without the necessity of a back-up driver such as Arizona, California, Michigan, and Ohio [13]. Effective testing and simulation environment is a vital part in the research of self-driving cars, which can test selfdriving software and hardware quickly in different virtual environments at low cost [33]. Ten officially designated autonomous vehicle test sites designed and supported by the US Department of Transportation, one of which is GoMentum Station in California with 20 miles of paved roads and a cluster of barracks and buildings in an urban environment, provide grounds to help set the direction of policy-making and testing procedures for FAGVs [34].…”
Section: Companymentioning
confidence: 99%
“…Leveraging computer vision for self-driving cars has evolved with the expanding requirements and research in the field and is now spread across several tasks, including vehicle detection, anomaly detection, trajectory prediction, object classification, path planning, collision avoidance, and modeling traffic rules [1,2]. As most of these systems are usually tested under simulations, the development and training under complex scenarios can be simulated using a variety of techniques, including modeling traffic using inspiration from the theory of multiagent systems, blocking and overtaking scenarios using RC cars, and an autoencoder trained with generative adversarial costs coupled with a recurrent neural network transition model [8][9][10][11].…”
Section: Related Workmentioning
confidence: 99%
“…Self-driving cars (Sec. VIII-C) Maqueda, A. I., et al [171] Steering Prediction 2018 Chen, S., et al [172] Testing platform 2019 Chernikova, A., et al [173] Security 2019 Ndikumana, A., et al [174] Caching for MEC 2020 Extended Reality (XR) (Sec. VIII-D) Liu, Y., et al [175] MEC-assisted VR 2018 Doumanoglou, A., et al [176] Quality of Experience 2018 van der Hooft, J., et al [177] Adaptive VR services 2019 van der Hooft, J., et al [178] Point cloud compression 2019 Industrial IoT (IIoT) (Sec.…”
Section: A Overviewmentioning
confidence: 99%