Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

Zhang, Shanxin; Wang, Cheng; Lin, Lili; Wen, Chenglu; Yang, Chenhui; Zhang, Zhemin; Li, Jonathan

doi:10.3390/rs11121453

Cited by 26 publications

(18 citation statements)

References 56 publications

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“…Safe driving and accident avoidance can be ensured when a timely visual recognition of traffic signs takes place [21,39,40]. Zhang et al [41] clustered the factors that affect a traffic sign's visual recognizability into three groups: (1) geometric factors of traffic sign, such as sign's size, placement, mounting height, aim, depression angle, occlusion angle, curvature of road and changing road surface [41]; (2) movement factors of vehicle, such as speed, Geometric Field Of View (GFOV), which decreases with speed being increased [42], and height and direction of line of sight which governs whether the traffic sign is contained by the GFOV; and (3) other factors, for example, weather conditions [43], lighting conditions [44], drivers' view reaction time which is affected by drivers' age and sight capabilities [44], and traffic density cognitive burden [45]. Moreover, occlusion may cause interruptions to visual continuity, thus affecting the recognition of the traffic signs [46].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Traffic Sign Comprehension Levels among Drivers in the Emirate of Abu Dhabi, UAE

Al-Rousan

Umar

2021

Infrastructures

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Road traffic signs are part of the road infrastructure components meant to ensure the safe use of roads by motorists and pedestrians alike. A good knowledge and comprehension of road signs helps ensure smooth flow of traffic, improves safety for other motorists and provides drivers with good reaction time to comply with the message dictated by the signs. Abu Dhabi, being the capital of the UAE, brings together people from all over the world for work, business or tourism. Abu Dhabi has quality roads and traffic signage together with an efficient police force that enforces adherence to traffic rules. Despite all these and the reduced fatalities in absolute terms, traffic violations have been increasing exponentially, resulting in five million traffic fines in 2019 for a population of about 2.9 million inhabitants. This study sought to assess motorists’ comprehension of the various traffic signs used across the UAE. The results of the survey (N=200) revealed that the drivers were able to correctly identify 77% of the road signs in the survey. It was also found that the respondents failed to correctly identify traffic signs within the specific category referred to as “advance warning signs”. Respondents’ education, nationality, gender, and marital status did not have any statistically significant effects on the results compared to earlier studies. It is recommended that more attention be focused on driver education and training.

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Section: Discussionmentioning

confidence: 99%

Assessment of Traffic Sign Comprehension Levels among Drivers in the Emirate of Abu Dhabi, UAE

Al-Rousan

Umar

2021

Infrastructures

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“…Over the past five years, mobile laser scanning (MLS) data have been recognized as a reliable data source for conducting visibility-related analyses [15][16][17][18]. MLS point clouds enable a very accurate and precise representation of real-world environment.…”

Section: Overview Of Visibility Modelingmentioning

confidence: 99%

Visibility-Based Technologies and Methodologies for Autonomous Driving

Easa¹,

Ma²,

Elshorbagy³

et al. 2021

Artificial Intelligence

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The three main elements of autonomous vehicles (AV) are orientation, visibility, and decision. This chapter presents an overview of the implementation of visibility-based technologies and methodologies. The chapter first presents two fundamental aspects that are necessary for understanding the main contents. The first aspect is highway geometric design as it relates to sight distance and highway alignment. The second aspect is mathematical basics, including coordinate transformation and visual space segmentation. Details on the Light Detection and Ranging (Lidar) system, which represents the ‘eye’ of the AV are presented. In particular, a new Lidar 3D mapping system, that can be operated on different platforms and modes for a new mapping scheme is described. The visibility methodologies include two types. Infrastructure visibility mainly addresses high-precision maps and sight obstacle detection. Traffic visibility (vehicles, pedestrians, and cyclists) addresses identification of critical positions and visibility estimation. Then, an overview of the decision element (path planning and intelligent car-following) for the movement of AV is presented. The chapter provides important information for researchers and therefore should help to advance road safety for autonomous vehicles.

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“…A survey of different techniques designed for detection and handling of occlusion in images was analyzed in [3]. Traffic Sign Visual Recognizability Evaluation Model (TSVREM) was developed in [22] for enhancing traffic safety. But, the occlusion detection was not improved.…”

Section: Literature Surveymentioning

confidence: 99%

Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

Arun

Kumar

2021

Multimed Tools Appl

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Occlusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional techniques do not recognize traffic signs well due to the vehicles are occluded. Besides occlusion removal was not performed in existing techniques with a less amount of time. In order to overcome such limitations, Non-linear Gaussian Bilateral Filtered Sorenson–Dice Exemplar Image Inpainting Based Bayes Conditional Probability (NGBFSEII-BCP) Method is proposed. Initially, a number of remote sensing images are taken as input from Highway Traffic Dataset. Then, the NGBFSEII-BCP method applies the Non-Linear Gaussian Bilateral Filtering (NGBF) algorithm for removing the noise pixels in input images. After preprocessing, the NGBFSEII-BCP method is used to remove the occlusion in the input images. Finally, NGBFSEII-BCP Method applies Bayes conditional probability to find operation status and thereby gets higher road safety using remote sensing images. The technique conducts the simulation evaluation using metrics such as peak signal to noise ratio, computational time, and detection accuracy. The simulation result illustrates that the NGBFSEII-BCP Method increases the detection accuracy by 20% and reduces the computation time by 32% as compared to state-of-the-art works.

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Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

Cited by 26 publications

References 56 publications

Assessment of Traffic Sign Comprehension Levels among Drivers in the Emirate of Abu Dhabi, UAE

Assessment of Traffic Sign Comprehension Levels among Drivers in the Emirate of Abu Dhabi, UAE

Visibility-Based Technologies and Methodologies for Autonomous Driving

Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

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