Sensing Techniques and Detection Methods for Train Approach Detection

Santos, João A.; Hempel, Michael; Sharif, Hamid

doi:10.1109/vtcfall.2013.6692407

Cited by 10 publications

(8 citation statements)

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“…Although we could further optimize image processing to achieve higher frame rates or enable higher resolutions, this does not give these algorithms the ability to address line-of-sight obstructions and blind spots, nor does it enable enhanced visibility under adverse weather conditions. However, a more suitable approach, as used in our own work and shown in Figure 1, is to utilize a multi-sensory detection methodology that uses different sensor technologies to mutually augment each other [20][21][22].…”

Section: Performance Of the Proposed Algorithmmentioning

confidence: 99%

A Novel Method of Near-Miss Event Detection with Software Defined RADAR in Improving Railyard Safety

Banerjee

Santos

Hempel

et al. 2019

Safety

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Railyards are one of the most challenging and complex workplace environments in any industry. Railyard workers are constantly surrounded by dangerous moving objects, in a noisy environment where distractions can easily result in accidents or casualties. Throughout the years, yards have been contributing 20–30% of the total accidents that happen in railroads. Monitoring the railyard workspace to keep personnel safe from falls, slips, being struck by large object, etc. and preventing fatal accidents can be particularly challenging due to the sheer number of factors involved, such as the need to protect a large geographical space, the inherent dynamicity of the situation workers find themselves in, the presence of heavy rolling stock, blind spots, uneven surfaces and a plethora of trip hazards, just to name a few. Since workers spend the majority of time outdoors, weather conditions also play an important role, i.e., snow, fog, rain, etc. Conventional sensor deployments in yards thus fail to consistently monitor this workspace. In this paper, the authors have identified these challenges and addressed them with a novel detection method using a multi-sensor approach. They have also proposed novel algorithms to detect, classify and remotely monitor Employees-on-Duty (EoDs) without hindering real-time decision-making of the EoD. In the proposed solution, the authors have used a fast spherical-to-rectilinear transform algorithm on fish-eye images to monitor a wide area and to address blindspots in visual monitoring, and employed Software-Defined RADAR (SDRADAR) to address the low-visibility problem. The sensors manage to monitor the workspace for 100 m with blind detection and classification. These algorithms have successfully maintained real-time processing delay of ≤0.1 s between consecutive frames for both SDRADAR and visual processing.

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Section: Performance Of the Proposed Algorithmmentioning

confidence: 99%

A Novel Method of Near-Miss Event Detection with Software Defined RADAR in Improving Railyard Safety

Banerjee

Santos

Hempel

et al. 2019

Safety

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“…Thus, the early detection and warning of train arrivals are essential for the safety and security of railway operations. To date, different systems based on sensing techniques [1] have been applied for train arrival detection (TAD). The traditional commercialized methods include the use of treadle mechanisms, inductive sensors, and infrared beam sensors for axle-counting and determining the direction information from the approaching train.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Train Horn Detection for Railway Transit Safety

Tran

Tsai

Furletov

et al. 2022

Sensors

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The train horn sound is an active audible warning signal used for warning commuters and railway employees of the oncoming train(s), assuring a smooth operation and traffic safety, especially at barrier-free crossings. This work studies deep learning-based approaches to develop a system providing the early detection of train arrival based on the recognition of train horn sounds from the traffic soundscape. A custom dataset of train horn sounds, car horn sounds, and traffic noises is developed to conduct experiments and analysis. We propose a novel two-stream end-to-end CNN model (i.e., THD-RawNet), which combines two approaches of feature extraction from raw audio waveforms, for audio classification in train horn detection (THD). Besides a stream with a sequential one-dimensional CNN (1D-CNN) as in existing sound classification works, we propose to utilize multiple 1D-CNN branches to process raw waves in different temporal resolutions to extract an image-like representation for the 2D-CNN classification part. Our experiment results and comparative analysis have proved the effectiveness of the proposed two-stream network and the method of combining features extracted in multiple temporal resolutions. The THD-RawNet obtained better accuracies and robustness compared to those of baseline models trained on either raw audio or handcrafted features, in which at the input size of one second the network yielded an accuracy of 95.11% for testing data in normal traffic conditions and remained above a 93% accuracy for the considerable noisy condition of-10 dB SNR. The proposed THD system can be integrated into the smart railway crossing systems, private cars, and self-driving cars to improve railway transit safety.

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“…Also, there is possibility of failure of communication links and devices. This could have serious implications on worker and equipment safety and fatalities have been reported [1,2]. In 2013, two Bay Area Rapid Transit workers were fatally struck by a train while they were inspecting the tracks.…”

Section: Introductionmentioning

confidence: 99%

“…A comprehensive survey of such detection schemes is presented in [1]. However, there are several practical issues in using these technologies.…”

Section: Introductionmentioning

confidence: 99%

A classification tree-based system for multi-sensor train approach detection

Shrestha

Hempel

Rezaei

et al. 2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

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Personnel safety is an integral element of any railroad operation. Rail tracks need to be regularly maintained, which often requires rail workers to be physically present on possibly live tracks along with their equipment. This results in hazardous work conditions for the workers. To ensure worker safety a reliable system for detecting oncoming trains and alerting the workers, while giving them sufficient time to disengage from the worksite, is essential. In this paper, we present a multiple sensor based system that integrates the sensor elements with a signal processing unit for this purpose. The processing unit consists of a signal conditioning unit, a data processing unit and a machine learning framework. The conditioning unit prepares the acquired signals for later operations. The data processing unit extracts fingerprints from the reported signals that are later used by the machine learning framework as training and testing samples. The machine framework is a binary tree that classifies the event under investigation as presence or absence of a train on the track under observation. We show that the system is very accurate and can alert the workers under five seconds after the arrival of the train at the test site.

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Sensing Techniques and Detection Methods for Train Approach Detection

Cited by 10 publications

References 0 publications

A Novel Method of Near-Miss Event Detection with Software Defined RADAR in Improving Railyard Safety

A Novel Method of Near-Miss Event Detection with Software Defined RADAR in Improving Railyard Safety

End-to-End Train Horn Detection for Railway Transit Safety

A classification tree-based system for multi-sensor train approach detection

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