Assistive Intelligent Transportation Systems: The Need for User Localization and Anonymous Disability Identification

Llorca, David Fernández; Mínguez, R.; Alonso, Ignacio Parra; Lopez, Carlos Fernandez; Daza, Iván García; Sotelo, Miguel Ángel; Cordero, Cristina Alén

doi:10.1109/mits.2017.2666579

Cited by 19 publications

(7 citation statements)

References 32 publications

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“…Vision sensors, among these new techniques, have been broadly applied for civil engineering problems. Famous applications of vision‐sensing techniques include dynamic displacement monitoring (Cha et al., ; Park et al., ; Yoon et al., ), three‐axes (i.e., X‐axis, Y‐axis, and depth) displacement measurement (Park et al., ; Abdelbarr et al., ), surface displacement/strain measurement (Luo et al., ; Almeida et al., ), vision‐based structural analysis (Chen et al., ; Sharif et al., ; Park et al., ), cable tensile force evaluation (Kim et al., ), bridge‐lining inspection (Zhu et al., ), rocking motion and landslide monitoring (Debella‐Gilo and Kääb, ; Greenbaum et al., ), automatic construction progress assessment (Bügler et al., ), 3D object finding in point cloud (Sharif et al., ), surface crack/defection detection based on texture‐based video processing (Cord and Chambon, ; Chen et al., ) or deep learning (Cha et al., ; Cha et al, ; Zhang et al., ), vehicle classification based on spectrogram features (Yeum et al., ), and intelligent transportation (Chen et al., ; Fernandez‐Llorca et al., ). With advancement in image sensors and computer techniques such as computer vision, cloud computing, and wireless data transfer, vision sensors have become more cost‐effective and computation‐efficient, thus have high potential in field application for SHM problems.…”

Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

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Computer vision‐based displacement measurement for structural monitoring has grown popular. However, tracking natural low‐contrast targets in low‐illumination conditions is inevitable for vision sensors in the field measurement, which poses challenges for intensity‐based vision‐sensing techniques. A new edge‐enhanced‐matching (EEM) technique improved from the previous orientation‐code‐matching (OCM) technique is proposed to enable robust tracking of low‐contrast features. Besides extracting gradient orientations from images as OCM, the proposed EEM technique also utilizes gradient magnitudes to identify and enhance subtle edge features to form EEM images. A ranked‐segmentation filtering technique is also developed to post‐process EEM images to make it easier to identify edge features. The robustness and accuracy of EEM in tracking low‐contrast features are validated in comparison with OCM in the field tests conducted on a railroad bridge and the long‐span Manhattan Bridge. Frequency domain analyses are also performed to further validate the displacement accuracy.

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Section: Introductionmentioning

confidence: 99%

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Luo

Feng

2018

Computer aided Civil Eng

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“…A review of computer vision techniques based on the Kinect sensor is proposed in [101]. [19] Disparity error of 1/10 pixel (correspond to about 1m distance error if the object is 100m far away) [169] INFRARED From a few cm to several meters [85], [108] Temperature accuracy of +/-1 • C, can measure temperatures up to 3,000 • C [85] ULTRASONIC From 2cm to 500cm [36], [195] About 0.3mm [36], [195] RFID Several meters [256], [84] A few centimeters [256], [84] LIDAR Up to 300m [251], [193] Up to 2cm [62], [193 [62], [160], [204] • Short range: Less than 0.15m or 1% [160], [103], [100] • Middle range: Less than 0.3m or 1% [160], [103] • Long range: 0.1m e.g. Bosch LRR3 77 GHz, range 250m [62] d) Active Ultrasonic Sensors: They emit sound waves and a detector senses the sound waves reflected by passing objects.…”

Section: B) Radar (Radio Detection and Ranging)mentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.

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“…The metric to evaluate the best adjustment is calculated using equations (1) and (2), where the precision and recall are computed by matching the model and the sensing of the environment. The matching is evaluated in 4 different groups ( ).…”

Section: Road Map Modelingmentioning

confidence: 99%

“…play an important role in the probability of crash or the final outcome. In order to reduce human errors and improve traffic efficiency and safety, Assistance Intelligent Transportation Systems (AITS) [2], Advanced Driver Assistance System (ADAS), and Autonomous Driving (AD) were developed.…”

Section: Introductionmentioning

confidence: 99%

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

Fernández

Muñoz-Bulnes²,

Llorca

et al. 2018

Journal of Advanced Transportation

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This paper addresses the problem of high-level road modeling for urban environments. Current approaches are based on geometric models that fit well to the road shape for narrow roads. However, urban environments are more complex and those models are not suitable for inner city intersections or other urban situations. The approach presented in this paper generates a model based on the information provided by a digital navigation map and a vision-based sensing module. On the one hand, the digital map includes data about the road type (residential, highway, intersection, etc.), road shape, number of lanes, and other context information such as vegetation areas, parking slots, and railways. On the other hand, the sensing module provides a pixelwise segmentation of the road using a ResNet-101 CNN with random data augmentation, as well as other hand-crafted features such as curbs, road markings, and vegetation. The high-level interpretation module is designed to learn the best set of parameters of a function that maps all the available features to the actual parametric model of the urban road, using a weighted F-score as a cost function to be optimized. We show that the presented approach eases the maintenance of digital maps using crowd-sourcing, due to the small number of data to send, and adds important context information to traditional road detection systems.

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Assistive Intelligent Transportation Systems: The Need for User Localization and Anonymous Disability Identification

Cited by 19 publications

References 32 publications

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps

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