Future vehicles: interactive wheeled robots

Ma, Nan; Li, Deyi; He, Wen; Deng, Yue; Li, Jiahong; Gao, Yue; Bao, Hong; Zhang, Huan; Xu, Xinkai; Liu, Yuansheng; Wu, Zhixuan; Chen, Li

doi:10.1007/s11432-020-3171-4

Cited by 36 publications

(7 citation statements)

References 11 publications

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“…As one of the essential signs of the third wave of artificial intelligence, wheeled robots not only inherit knowledge but also learn independently, which brings about to enable wheeled robots that use a driving brain to achieve data-driven control and learning [10]. In the process of learning and interaction of wheeled robots, the privacy protection of the generated data will face substantial challenges due to its large data volume and diverse structure, and the privacy computing field will be paid additional attention in the future [11].…”

Section: Privacy Protection Based On Privacy Computingmentioning

confidence: 99%

Retracted: Multi‐vehicle group‐aware data protection model based on differential privacy for autonomous sensor networks

Yuan

Wang

et al. 2022

IET Circuits, Devices & Syst

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Retraction: [Jiazheng Yuan, Zhuang Wang, Cheng Xu, Hongtian Li, Songyin Dai, Hongzhe Liu, Multi‐vehicle group‐aware data protection model based on differential privacy for autonomous sensor networks, IET Circuits, Devices & Systems 2022 (https://doi.org/10.1049/cds2.12140)].The above article from IET Circuits, Devices & Systems, published online on 29 December 2022 in Wiley Online Library (https://wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Harry E. Ruda, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer revie process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Privacy Protection Based On Privacy Computingmentioning

confidence: 99%

Retracted: Multi‐vehicle group‐aware data protection model based on differential privacy for autonomous sensor networks

Yuan

Wang

et al. 2022

IET Circuits, Devices & Syst

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“…There are various research directions and design schemes, including DPT [20] model, DAT [21] model, Deformable DETR [22] model, etc., among which we refer to the idea of DAT model. It is not designed by directly introducing DCN in the computation of QKV, which will lead to a sharp increase in space complexity, but through the offset sub-network consisting of Depthwise convolution, SyncBatchNorm [23] and GELU [24] to compute 2D offsets and through the reference point with the 2D offsets to generate the offset feature maps, and, finally, from this, K and V are computed. As a result, deformable attention possesses both the ability to extract global contextual information and the ability to fit local spatial information, which meets the needs of fisheye images.…”

Section: Brief Description Of Deformable Attentionmentioning

confidence: 99%

DSA: Deformable Segmentation Attention for Multi-Scale Fisheye Image Segmentation

Jiang,

Xu,

Liu

et al. 2023

Electronics

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With a larger field of view (FOV) than ordinary images, fisheye images are becoming mainstream in the field of autonomous driving. However, the severe distortion problem of fisheye images also limits its application. The performance of neural networks designed for narrow FOV images degrades drastically for fisheye images, and the use of large composite models can improve the performance, but it brings huge time overhead and hardware costs. Therefore, we decided to balance real time and accuracy by designing the deformable segmentation attention(DSA) module, a generalpurpose architecture based on a deformable attention mechanism and a spatial pyramid architecture. The deformable mechanism serves to accurately extract feature information from fisheye images, together with attention to learn the global context and the spatial pyramid structure to balance multiscale feature information, thus improving the perception of fisheye images by traditional networks without increasing the amount of excessive computation. Lightweight networks such as SegNeXt equipped with the DSA module enable effective and rapid multi-scale segmentation of fisheye images in complex scenes. Our architecture achieves outstanding results on the WoodScape dataset, while our ablation experiments demonstrate the effectiveness of various parts of the architecture.

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“…The SPID effectively realizes the recovery of vibration energy of automobile exhaust devices and the self-energy supply of sensors and signal monitoring functions. This device is expected to play a crucial role in the field of intelligent driving and automobile intelligence [ 50 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Device for Harvesting the Vibration Energy of the Automobile Exhaust with a Piezoelectric Generator

Huang

et al. 2023

Micromachines

Self Cite

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With increasing consumption of energy and increasing environmental pollution, research on capturing the vibration energy lost during transportation and vehicle driving is growing rapidly. There is a large amount of vibration energy in the automobile exhaust system that can be recycled. This paper proposes a self-powered intelligent device (SPID) using a piezoelectric energy generator. The SPID includes a piezoelectric generator and sensor unit, and the generator is installed at the end of the automobile exhaust system. The generator adopts a parallel structure of four piezoelectric power generation units, and the sensing unit comprises light-emitting diode warning lights or low-power sensors. A simulated excitation experiment verifies the working state and peak power of the piezoelectric generator unit, which can achieve 23.4 μW peak power. The self-power supply and signal monitoring functions of the intelligent device are verified in experiments conducted for driving light-emitting diode lights and low-power sensors. The device is expected to play a crucial role in the field of intelligent driving and automobile intelligence.

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Future vehicles: interactive wheeled robots

Cited by 36 publications

References 11 publications

Retracted: Multi‐vehicle group‐aware data protection model based on differential privacy for autonomous sensor networks

Retracted: Multi‐vehicle group‐aware data protection model based on differential privacy for autonomous sensor networks

DSA: Deformable Segmentation Attention for Multi-Scale Fisheye Image Segmentation

Intelligent Device for Harvesting the Vibration Energy of the Automobile Exhaust with a Piezoelectric Generator

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