Safe Driving at Traffic Lights: An Image Recognition Based Approach

Bao, Cuizhu; Chen, Chen; Kui, Hailin; Wang, Xiaoyang

doi:10.1109/mdm.2019.00-67

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…Each configuration has a meta-architecture selection model, with an extractor characteristic of the model (RESNET and INCEPTION RESNET), including resolutions for inputs, or a number of proposals to increase R-CNN and R-FCN processing. Another method used to obtain results as needed and efficiently parameterized is the cascade operation mode, so the browsing time decreases by completing a much more efficient subset of data [59,60]. Tests conclude that R-FCN and SSD models offer better speeds as opposed to R-CNN, which tends to handle slower models but at the same time offers significantly higher accuracy, even if the processing time sometimes reaches 100 ms for each image processed.…”

Section: Description Pedestrian Setup and Practical Scenariosmentioning

confidence: 99%

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Zadobrischi

Dimian

2021

Remote Sensing

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Vehicle density and technological development increase the need for road and pedestrian safety systems. Identifying problems and addressing them through the development of systems to reduce the number of accidents and loss of life is imperative. This paper proposes the analysis and management of dangerous situations, with the help of systems and modules designed in this direction. The approach and classification of situations that can cause accidents is another feature analyzed in this paper, including detecting elements of a psychosomatic nature: analysis and detection of the conditions a driver goes through, pedestrian analysis, and maintaining a preventive approach, all of which are embedded in a modular architecture. The versatility and usefulness of such a system come through its ability to adapt to context and the ability to communicate with traffic safety systems such as V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2X (vehicle-to-everything), and VLC (visible light communication). All these elements are found in the operation of the system and its ability to become a portable device dedicated to road safety based on (radio frequency) RF-VLC (visible light communication).

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Section: Description Pedestrian Setup and Practical Scenariosmentioning

confidence: 99%

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Zadobrischi

Dimian

2021

Remote Sensing

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“…Our first task was to build a recogniser for TLs. As noted, this problem has been addressed by a variety of researchers [5][6][7][8][9] and we drew upon previous work. In our case, we need a recogniser that would (i) handle different perspectives of TLs and (ii) whose output could be integrated with the computation of gaze, namely, identify a region in the image corresponding to the TL.…”

Section: Traffic Light Recognitionmentioning

confidence: 99%

“…While the detection and identification of TLs is a key part of the model and there has been substantial work on methods to do this [5][6][7][8][9], this is not the central focus of this paper. For driver modelling, the output from the detection processing must be integrated with analyses of the driver's gaze.…”

Section: Introductionmentioning

confidence: 99%

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Predicting driver behaviour at intersections based on driver gaze and traffic light recognition

Rahman

Beauchemin

Bauer

2020

IET intell. transp. syst

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This work introduces and evaluates a model for predicting driver behaviour, namely turns or proceeding straight, at traffic light intersections from driver three-dimensional gaze data and traffic light recognition. Based on vehicular data, this work relates the traffic light position, the driver's gaze, head movement, and distance from the centre of the traffic light to build a model of driver behaviour. The model can be used to predict the expected driver manoeuvre 3 to 4 s prior to arrival at the intersection. As part of this study, a framework for driving scene understanding based on driver gaze is presented. The outcomes of this study indicate that this deep learning framework for measuring, accumulating and validating different driving actions may be useful in developing models for predicting driver intent before intersections and perhaps in other key-driving situations. Such models are an essential part of advanced driving assistance systems that help drivers in the execution of manoeuvres.

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“…Throughout the years, an increment in the number of cars on roads has increased the frequency of causalities. This endangers human life and safety therefore computer vision techniques are needed to be utilized for observing the immediate data in real-time [15]. Existing studies reveal various intrusive and non-intrusive such as in situ techniques and invehicle technologies that are used for traffic monitoring.…”

Section: Introductionmentioning

confidence: 99%

Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN

Sahar

Khadami²,

Rauf³

2023

SJET

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Smart city infrastructure and Intelligent Transportation Systems (ITS) need modern traffic monitoring and driver assistance systems such as autonomous traffic signal detection. ITS is a dominant research area among several fields in the domain of artificial intelligence. Traffic signal detection is a key module of autonomous vehicles where accuracy and inference time are amongst the most significant parameters. In this regard, the aim of this study is to detect traffic signals focusing to enhance accuracy and real-time performance. The results and discussion enclose a comparative performance of a CNN-based algorithm YOLO V3 and a handcrafted technique that gives insight for enhanced detection and inference in day and night light. It is important to consider that real-world objects are associated with complex backgrounds, occlusion, climate conditions, and light exposure that deteriorate the performance of sensitive intelligent applications. This study provides a direction to propose a hybrid technique for TLD not only in the daytime but also in night light.

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Safe Driving at Traffic Lights: An Image Recognition Based Approach

Cited by 9 publications

References 8 publications

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies

Predicting driver behaviour at intersections based on driver gaze and traffic light recognition

Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN

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