Road surface detection and recognition for route recommendation

Dai, Jyun-Min; Liu, Tse-An Jacky; Lin, Huei-Yung

doi:10.1109/ivs.2017.7995708

Cited by 13 publications

(3 citation statements)

References 18 publications

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“…More and more artificial intelligence and deep learning methods are applied to this route recommendation problem. Convolution Neural Network (CNN), which is widely used in image processing, is utilized to detect and recognize road surface for route recommendation [56] . Rrecurrent Neural Network (RNN) [57] based default logic is proposed for route planning to improve the accuracy of default reasoning in a dynamic environment.…”

Section: Machine Learning-based Route Recommendationmentioning

confidence: 99%

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Luo

et al. 2020

Intell. and Converged Netw.

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Internet of Vehicles (IoV) is a distributed network of connected cars, roadside infrastructure, wireless communication networks, and central cloud platforms. Wireless recommendations play an important role in the IoV network, for example, recommending appropriate routes, recommending driving strategies, and recommending content. In this paper, we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.

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Section: Machine Learning-based Route Recommendationmentioning

confidence: 99%

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Luo

et al. 2020

Intell. and Converged Netw.

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“…Rezaee and Zhang (2017) conducted a patchedbased Deep Neural Network (DNN) model to detect roads on high spatial resolution orthophoto aerial data. Dai et al (2017) applied the K-mean algorithm to divide the objects into road, sky, and background as well as using the Convolution Neural Network (CNN) to classify different road types. Instead of implementing deep learning methods and processing the images directly, Yang et al (2012) generated the georeferenced image from laser points to represent the geospatial information of surroundings.…”

Section: Road S Urface Extractionmentioning

confidence: 99%

The Assessment of Curved Centerline Generation in Hdmaps Based on Point Clouds

Zeng

Chiang

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Over the decades, autonomous driving technology has attracted a lot of attention and is under rapid development. However, it still suffers from inadequate accuracy in a certain area, such as the urban area, Global Navigation Satellite System (GNSS) hostile area, due to the multipath interference or Non-Line-of-Sight (NLOS) reception. In order to realize fully autonomous applications, High Definition Maps (HD Maps) become extra assisted information for autonomous vehicles to improve road safety in recent years. Compared with the conventional navigation maps, the accuracy requirement in HD Maps, which is 20 cm in the horizontal direction and 30 cm in 3D space, is considerably higher than the conventional one. Additionally, HD Maps consist of rich and high accurate road traffic information and road elements. For the requirement of high accuracy, conducting a Mobile Laser Scanning (MLS) system is an appropriate method to collect the geospatial data accurately and efficiently. Nowadays, digital vector maps are constructed by digitalizing manually on the collected data. However, the manual process spends a lot of manpower and is not efficient and practical for a large field. Therefore, this paper proposes to automatically construct the crucial road elements, such as road edge, lane line, and centerline, to generate the HD Maps based on point clouds collected by the MMS from the surveying company. The RMSEs in the horizontal direction of the road edge, lane line, and centerline are all lower than 30 cm in 3D space.

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“…Also, they have identified the damage condition of road surfaces. A very interesting and fundamental work was done by Dai et al [12] wherein the street view path images are downloaded, the vanishing image points are identified and then the road surfaces are identified. The brick and asphalt road surface images are considered as training data set.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Approach for Classification of Road Surfaces

Prusty¹,

Mohanty²

2023

RIA

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We propose a novel algorithm for classification of road surfaces which can be used in intelligent vehicle for helping the driver. In a broad sense the road surfaces are classified as smooth and rough road surface. Smooth road surfaces are cement, sand and asphalt road surface where as rough road surfaces are rough asphalt and grass road surfaces. Two features are being proposed to classify smooth and rough road surfaces by analysing the intensity pattern of road surfaces and then they are classified with the help of a probabilistic classifier. After that lane detection method is used to distinguish sand road from asphalt and cement road surfaces. Then simple image processing techniques like edge and histogram analysis are used to identify remaining individual road surfaces. All works are being done in intensity plane.

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Road surface detection and recognition for route recommendation

Cited by 13 publications

References 18 publications

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

The Assessment of Curved Centerline Generation in Hdmaps Based on Point Clouds

A Machine Learning Based Approach for Classification of Road Surfaces

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