Autonomous vehicles testing methods review

Huang, Weiping; Wang, Kunfeng; Lv, Yisheng; Zhu, Fenghua

doi:10.1109/itsc.2016.7795548

Cited by 147 publications

(67 citation statements)

References 17 publications

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“…This cluster mainly discusses testing methods of autonomous driving and cybersecurity risks, which are introduced in an overview by Huang et al. (2016) .…”

Section: Resultsmentioning

confidence: 99%

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Mora

Guo

Panori³

2020

Journal of Cleaner Production

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Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modelling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions.

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“…This cluster mainly discusses testing methods of autonomous driving and cybersecurity risks, which are introduced in an overview by Huang et al. (2016) .…”

Section: Resultsmentioning

confidence: 99%

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Mora

Guo

Panori³

2020

Journal of Cleaner Production

View full text Add to dashboard Cite

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“…A lot of test studies for C-V2X have been carried out all over the world. But it is still a difficult work to test the C-V2X functionalities and performance for the typical V2X use cases in the field testing scenarios [34]. The digital twin provides virtual representations of systems along their lifecycle and should be developed to a good method for C-V2X based autonomous driving test.…”

Section: C-v2xmentioning

confidence: 99%

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

1,254

495

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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“…The architecture of an AV system consists of a perception, decision, and action layer (Huang, Wang, Lv, & Zhu, ). The perception layer or “making sense of the world” is one of the most difficult tasks of the autonomous system given the complexity of road traffic environments (Anderson et al., ).…”

Section: Emerging Technology Risksmentioning

confidence: 99%

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

2018

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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 historical data to quantify insurable risks. This article investigates the risk structure of SAVs and employs a telematics-based anomaly detection model to assess split risk profiles. An unsupervised multivariate Gaussian (MVG) based anomaly detection method is used to identify abnormal driving patterns based on accelerometer and GPS sensors of manually driven vehicles. Parameters are inferred for vehicles equipped with semiautonomous capabilities and the resulting split risk profile is determined. The MVG approach allows for the quantification of vehicle risks by the relative frequency and severity of observed anomalies and a location-based risk analysis is performed for a more comprehensive assessment. This approach contributes to the challenge of quantifying SAV risks and the methods employed here can be applied to evolving data sources pertinent to SAVs. Utilizing the vast amounts of sensor-generated data will enable insurers to proactively reassess the collective performances of both the artificial driving agent and human driver.

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Autonomous vehicles testing methods review

Cited by 147 publications

References 17 publications

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Mind the gap: Developments in autonomous driving research and the sustainability challenge

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

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