ArticleSensors (Basel, Switzerland)2025
Automotive Radar Range Spectrum-Based Road Surface Classification by Using Machine Learning.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Real-Time Edge Computing for Road Surface Classification Using Multi-IMU Data and a Hybrid CNN-LSTM Classification Model.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
The awareness of different road surface types is crucial for the safe operation of automated vehicles in on- and off-road modes. This paper focuses on the classification of different road surface types using automotive radar and Machine Learning (ML) Artificial Intelligence (AI) models. This analysis is based on the range spectrum of the backscattered radar signals from different road surfaces. The dataset for training and testing is formed by combining the range data from various road surface types. A Random Forest (RF) classifier is built for the identification and classification of four different road surface types. The proposed method is compared using range data obtained from two different mounting positions of the radar. Under dry conditions, a generalization error of 84.5% in the forward-looking position of the radar is achieved. The proposed classifier is also able to distinguish between wet and dry asphalt surfaces with a generalization error of 88.7%.
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Registered trials
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