A novel regenerative braking energy recuperation system for electric vehicles based on driving style

Chengqun, Qiu; Wan, Xinshan; Wang, Na; Cao, Sunjia; Ji, Xinchen; Wu, Kun; Hu, Yaoyu; Meng, Mingyu

doi:10.1016/j.energy.2023.129055

Cited by 13 publications

(1 citation statement)

References 31 publications

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“…During testing, the VBFNet-YOLOv8 deep learning algorithm was employed to detect complex and dynamic road environments and perform hierarchical risk warnings. This system is a highly integrated framework [23,24] comprising the following major modules: the onboard camera unit, computing processing platform, safety protocol, and data recording and communication module. The onboard camera unit is responsible for real-time capturing of road conditions, including but not limited to the speed and position of vehicles ahead and the surrounding traffic environment.…”

Section: Testing 41 Test Deploymentmentioning

confidence: 99%

Deep Learning-based Visual Risk Warning System for Autonomous Driving

Qiu,

Tang,

et al. 2024

Preprint

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In autonomous driving, the identification and tracking of multiple vehicles on the road are critical tasks. This paper aims to develop a risk warning system using deep learning algorithms to address the heterogeneous, high-dynamic, and complex driving environments. To enhance the generalization capability and detection accuracy of small objects in road perception, we propose a novel VBFNet-YOLOv8 algorithm for real-time vehicle identification, tracking, distance measurement, and speed estimation. Specifically, we replace the Backbone of the original YOLOv8 network with the VanillaNet structure and upgrade the traditional PANet in the neck part to Bi-FPN. By integrating the optimized YOLOv8n algorithm with Deepsort and TTC algorithms, we achieve a comprehensive road risk assessment. The algorithm continuously tracks the targets, and the TTC algorithm intuitively assesses the risk. Finally, the system provides layered warnings by changing the color of the bounding boxes, offering drivers an integrated and real-time risk alert. Comparative experimental results show that the optimized algorithm improves Precision by 0.61%, mAP@0.5 by 0.63%, and mAP@0.5:0.95 by 0.70%. In the road tests on sections A and B, the detection frame rate of the risk warning system maintained a minimum of 37.1fps and a maximum of 56.4fps. The detection Confidence of various objects remained above 0.67, reaching up to 0.97.

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Section: Testing 41 Test Deploymentmentioning

confidence: 99%

Deep Learning-based Visual Risk Warning System for Autonomous Driving

Qiu,

Tang,

et al. 2024

Preprint

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Lightweight ViT with Multiscale Feature Fusion for Driving Risk Rating Warning System

Tang,

Xu,

et al. 2024

Advcd Theory and Sims

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Addressing the issue of inadequate dynamic object detection accuracy in current road driving warning systems, this study proposes the RepBF‐YOLOv8 detection algorithm aimed at efficient risk identification. The backbone network of YOLOv8n is replaced with the lightweight RepViT architecture, which is more suitable for visual tasks. This replacement simplifies the traditional structure, reduces the complexity of the backbone network, maximizes performance enhancement, and minimizes latency. Additionally, the FPN in the neck section is upgraded to Bi‐FPN, which reduces nodes and span connections and incorporates rapid normalization to achieve fast multi‐scale feature fusion. For risk grading, the algorithm infers distances and collision times, categorizing detected objects into high, medium, and low‐risk levels, and uses different colors to warn the driver. Comparative experimental results show that the optimized algorithm improves Precision by 1.7%, Recall by 2.3%, mAP@0.5 by 1.53%, and mAP@0.5:0.95 by 2.91%. In road tests, the risk warning system achieves a frame detection rate ranging from a minimum of 38.4 fps to a maximum of 59.0 fps. The detection confidence for various objects remains above 0.71, reaching as high as 0.98. Specifically, the “Car” confidence ranges from 0.81 to 0.98, demonstrating the accuracy and robustness of vehicle risk detection.

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