Lightweight Regression Model with Prediction Interval Estimation for Computer Vision-based Winter Road Surface Condition Monitoring

Ojala, Risto; Seppänen, Alvari

doi:10.1109/tiv.2024.3371104

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Thus, there is a demand for models that can classify road surface conditions automatically and accurately to facilitate precise detection and prediction. Several studies have focused on the winter road surface condition classification using the images captured by vehicle-mounted cameras [ 9 , 10 , 11 ]. The purpose of these studies is to help with the construction of autonomous vehicles; however, our purpose is to assist road managers in reducing winter-related traffic accidents using fixed-point cameras.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions

Moroto,

Maeda,

Togo

et al. 2024

Sensors

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This paper proposes a multimodal Transformer model that uses time-series data to detect and predict winter road surface conditions. For detecting or predicting road surface conditions, the previous approach focuses on the cooperative use of multiple modalities as inputs, e.g., images captured by fixed-point cameras (road surface images) and auxiliary data related to road surface conditions under simple modality integration. Although such an approach achieves performance improvement compared to the method using only images or auxiliary data, there is a demand for further consideration of the way to integrate heterogeneous modalities. The proposed method realizes a more effective modality integration using a cross-attention mechanism and time-series processing. Concretely, when integrating multiple modalities, feature compensation through mutual complementation between modalities is realized through a feature integration technique based on a cross-attention mechanism, and the representational ability of the integrated features is enhanced. In addition, by introducing time-series processing for the input data across several timesteps, it is possible to consider the temporal changes in the road surface conditions. Experiments are conducted for both detection and prediction tasks using data corresponding to the current winter condition and data corresponding to a few hours after the current winter condition, respectively. The experimental results verify the effectiveness of the proposed method for both tasks. In addition to the construction of the classification model for winter road surface conditions, we first attempt to visualize the classification results, especially the prediction results, through the image style transfer model as supplemental extended experiments on image generation at the end of the paper.

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Section: Introductionmentioning

confidence: 99%

Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions

Moroto,

Maeda,

Togo

et al. 2024

Sensors

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Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System

Subramanian,

Büker

2024

Eng

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Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments is significantly constrained by adverse weather conditions, limiting their operation to clear weather due to safety concerns. Ensuring that AVs remain within their designated Operational Design Domain (ODD) is a formidable challenge, making boundary monitoring strategies essential for safe navigation. This study explores the critical role of an ODD monitoring system (OMS) in addressing these challenges. It reviews various methodologies for designing an OMS and presents a comprehensive visualization framework incorporating trigger points for ODD exits. These trigger points serve as essential references for effective OMS design. The study also delves into a specific use case concerning ODD exits: the reduction in road friction due to adverse weather conditions. It emphasizes the importance of contactless computer vision-based methods for road condition estimation (RCE), particularly using vision sensors such as cameras. The study details a timeline of methods involving classical machine learning and deep learning feature extraction techniques, identifying contemporary challenges such as class imbalance, lack of comprehensive datasets, annotation methods, and the scarcity of generalization techniques. Furthermore, it provides a factual comparison of two state-of-the-art RCE datasets. In essence, the study aims to address and explore ODD exits due to weather-induced road conditions, decoding the practical solutions and directions for future research in the realm of AVs.

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Lightweight Regression Model with Prediction Interval Estimation for Computer Vision-based Winter Road Surface Condition Monitoring

Cited by 2 publications

References 27 publications

Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions

Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions

Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System

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