Retraction Notice: Advanced Scenario Generation for Calibration and Verification of Autonomous Vehicles

As driving scenarios and autonomous vehicles (AVs) become increasingly intricating, there is an increasing need for innovative frameworks that can enhance and test AV capabilities across diverse scenarios. At present, the design and validation for AVs predominantly rely on simulation software or real-road testing methodologies. However, these approaches possess inherent limitations, leading to inaccuracy, reduced efficiency, and potential hazards during the development process. This letter reports our first DHW (decentralized and hybrid workshop) on Scenarios Engineering (SE), that aims to calibrate and validate AV modules through advanced scenarios. Specifically, the DHW discusses combining SE with existing simulation software and real-road testing strategies, enabling developers to maximize the advantages of each approach while ensuring the safety and efficacy in the development of autonomous vehicles. Our findings demonstrate the benefits of enhancing realism of simulation scenarios in the aspects of both content and appearance. Furthermore, the use of scenarios engineering is explored in order to enhance the diversity and safety of real-road testing by means of integrating virtual and physical components.

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Publication Details

Journal
IEEE Transactions on Intelligent Vehicles
Published
2024-11-20
DOI
https://doi.org/10.1109/tiv.2024.3502133
Primary Topic
Safety Systems Engineering in Autonomy
Type
article
Field-Weighted Citation Impact
0.00
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article

Retraction Notice: Advanced Scenario Generation for Calibration and Verification of Autonomous Vehicles

Xiaoxiang Na, Siyu Teng, Xingyuan Dai, Bingzi Liu et al.
IEEE Transactions on Intelligent Vehicles
Safety Systems Engineering in Autonomy
article

Retraction Notice: Advanced Scenario Generation for Calibration and Verification of Autonomous Vehicles

Xiaoxiang Na, Siyu Teng, Xingyuan Dai, Bingzi Liu, Xuan Li, Fei‐Yue Wang
article en

Abstract

As driving scenarios and autonomous vehicles (AVs) become increasingly intricating, there is an increasing need for innovative frameworks that can enhance and test AV capabilities across diverse scenarios. At present, the design and validation for AVs predominantly rely on simulation software or real-road testing methodologies. However, these approaches possess inherent limitations, leading to inaccuracy, reduced efficiency, and potential hazards during the development process. This letter reports our first DHW (decentralized and hybrid workshop) on Scenarios Engineering (SE), that aims to calibrate and validate AV modules through advanced scenarios. Specifically, the DHW discusses combining SE with existing simulation software and real-road testing strategies, enabling developers to maximize the advantages of each approach while ensuring the safety and efficacy in the development of autonomous vehicles. Our findings demonstrate the benefits of enhancing realism of simulation scenarios in the aspects of both content and appearance. Furthermore, the use of scenarios engineering is explored in order to enhance the diversity and safety of real-road testing by means of integrating virtual and physical components.

IEEE Transactions on Intelligent Vehicles
Hong Kong Baptist University (HK), Chinese Academy of Sciences (CN), University of Cambridge (GB), Institute of Automation (CN), Peng Cheng Laboratory (CN)
Openalex Percentile: Top 75%
Safety Systems Engineering in Autonomy
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Retraction Notice: Advanced Scenario Generation for Calibration and Verification of Autonomous Vehicles — Xiaoxiang Na, Siyu Teng, et al. · IEEE Transactions on Intelligent Vehicles (2024) | TGRS Research Map | TGRS