A Hybrid Vision-Map Method for Urban Road Detection

Fernández, Carlos; Llorca, David Fernández; Sotelo, Miguel Ángel

doi:10.1155/2017/7090549

Cited by 17 publications

(24 citation statements)

References 40 publications

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“…An understanding of lanes, and how they are connected through merges and intersections remains a challenge from the perspective of perception. In this section, we provide an overview of current methods used for road and lane detection, and refer the reader to in-depth surveys of traditional methods [194] and the state-of-the-art methods [195], [196].…”

Section: Road and Lane Detectionmentioning

confidence: 99%

“…Stereo camera systems [204], as well as 3D lidars [201], have been used determine the 3D structure of roads directly. More recently, machine learningbased methods which either fuse maps with vision [196] or use fully appearance-based segmentation [205] have been used. We employed an open-source 1 deep spatiotemporal video-based risk detection framework [197] to assess the image sequences shown in this figure.…”

Section: Road and Lane Detectionmentioning

confidence: 99%

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A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS

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Section: Road and Lane Detectionmentioning

confidence: 99%

Section: Road and Lane Detectionmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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“…This is one of the advantages of analyzing the condition of the pavement with accelerometer data compared to computer vision based methods. Computer vision based methods are able to accurately detect the location of a road anomaly whatever the depth or severity but the irregularity cannot be fully captured and analyzed using computer vision based methods alone [6][7][8][9][10].…”

Section: Signal Processingmentioning

confidence: 99%

Pavement Condition Analysis via Vehicle Mounted Accelerometer Data

Mohiddeen

Rosli²,

Hanif³

et al. 2020

IIUMEJ

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Road anomalies and irregularities such as potholes and uneven surfaces are a common hazard in South East Asia and developing countries. Such hazards pose a threat to the safety and well-being of both civilians going about their daily routine and tourists who are exploring the city. Since bicycles and rickshaws are still a common mode of transport used by both civilians and tourists in many South East Asian countries, it is essential to improve the overall quality and smoothness of pavements which are traversed by these vehicles. Management of international sporting and recreational events also require satisfactory road and pavement conditions. Before pavement conditions can be improved, it is an essential prerequisite to obtain comprehensive information about road irregularities such as the location and also severity of the road irregularity (depth of the potholes and height of bumps). In this paper, we propose a method for obtaining mathematical models that represent the overall condition of the pavements that are part of a commonly traversed cycling route. Such mathematical models and coefficients can be stored in the cloud of an Internet of Things (IOT) data analytics systems subsequently leading to identification of regions with severe road irregularities. ABSTRAK: Kerosakan pada permukaan jalan raya merupakan salah satu faktor risiko kemalangan yang berlaku secara meluas di negara Asia Tenggara dan negara membangun yang lain. Memandangkan kenderaan seperti beca dan basikal masih diguna pakai secara meluas di negara membangun, adalah mustahak untuk membaik pulih kerosakan jalan raya. Pengurusan sukan antarabangsa dan rekreasi juga memerlukan keadaan jalan dan laluan pejalan kaki yang baik. Sebelum kerja membaik pulih dapat dilakukan, maklumat lengkap mengenai tahap kerosakan jalan raya dan lokasi kerosakan diperlukan. Dalam kajian ini satu kaedah telah diperkenalkan untuk mendapatkan persamaan matematik yang menggambarkan keadaan sebenar permukaan jalan raya, di mana sebahagiannya merupakan laluan berbasikal yang selalu digunakan. Model matematik dan pekali ini boleh di simpan dalam sistem analisis data awan Internet Benda (IOT) kemudiannya dapat mengenal pasti kawasan jalan yang rosak dan tidak rata.

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“…In Reference [45], shadow boundaries were detected by comparing edges in the input RGB image to edges found in the one-dimensional illumination-invariant shadow-free image obtained by the color-constancy method in Reference [46]. Despite the fact that this method is not reliable in images where shadow edges are not well defined [45], it was widely exploited [1,[47][48][49][50]. However, most of these illumination-invariant methods require user intervention, as well as high-quality images with wide dynamic range and calibrated sensors, failing severely with consumer-quality images [44].…”

Section: Introductionmentioning

confidence: 99%

Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination

Ibarra-Arenado

Tjahjadi

Pérez-Oria

2020

Sensors

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A well-known challenge in vision-based driver assistance systems is cast shadows on the road, which makes fundamental tasks such as road and lane detections difficult. In as much as shadow detection relies on shadow features, in this paper, we propose a set of new chrominance properties of shadows based on the skylight and sunlight contributions to the road surface chromaticity. Six constraints on shadow and non-shadowed regions are derived from these properties. The chrominance properties and the associated constraints are used as shadow features in an effective shadow detection method intended to be integrated on an onboard road detection system where the identification of cast shadows on the road is a determinant stage. Onboard systems deal with still outdoor images; thus, the approach focuses on distinguishing shadow boundaries from material changes by considering two illumination sources: sky and sun. A non-shadowed road region is illuminated by both skylight and sunlight, whereas a shadowed one is illuminated by skylight only; thus, their chromaticity varies. The shadow edge detection strategy consists of the identification of image edges separating shadowed and non-shadowed road regions. The classification is achieved by verifying whether the pixel chrominance values of regions on both sides of the image edges satisfy the six constraints. Experiments on real traffic scenes demonstrated the effectiveness of our shadow detection system in detecting shadow edges on the road and material-change edges, outperforming previous shadow detection methods based on physical features, and showing the high potential of the new chrominance properties.

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A Hybrid Vision-Map Method for Urban Road Detection

Cited by 17 publications

References 40 publications

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Pavement Condition Analysis via Vehicle Mounted Accelerometer Data

Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination

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