A Hybrid Deep Learning Approach for Vehicle Wheel Slip Prediction in Off-Road Environments

Basri, Mustofa; Karapetyan, Areg; Hassan, Bilal; Khonji, Majid; Dias, Jorge

doi:10.1109/rose56499.2022.9977432

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…For instance, in [44], wheel slip is discretized into categories of low slip, moderate slip, and high slip. Furthermore, regression evaluation of wheel slip has been performed using machine learning methods such as multilayer perceptron (MLP), random forest (RF), and extreme gradient boosting (XGB) [81].…”

Section: Safety Costmentioning

confidence: 99%

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A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments

Wang,

Li,

Zhang

et al. 2024

Machines

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Autonomous driving in unstructured environments is crucial for various applications, including agriculture, military, and mining. However, research in unstructured environments significantly lags behind that in structured environments, mainly due to the challenges posed by harsh environmental conditions and the intricate interactions between vehicles and terrains. This article first categorizes unstructured path planning into hierarchical and end-to-end approaches and then the special parts compared to structured path planning are emphatically reviewed, such as terrain traversability analysis, cost estimation, and terrain-dependent constraints. This article offers a comprehensive review of the relevant factors, vehicle–terrain interactions, and methods of terrain traversability analysis. The estimation methods of safety cost, energy cost, and comfort cost are also emphatically summarized. Moreover, the constraints caused by the limits of terrains and vehicles are discussed. The applications of algorithms in recent articles for path planners are reviewed. Finally, crucial areas requiring further research are analyzed in unstructured path planning.

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Section: Safety Costmentioning

confidence: 99%

“…Notably, the estimation model proposed in [81] demonstrates strong performance in both discrete and regression prediction of wheel slip.…”

Section: Safety Costmentioning

confidence: 99%