Time to collision estimation for vehicles coming from behind using in-vehicle camera

Cosic, Luka; Vranješ, Mario; Ilkic, Veljko; Mihic, Velibor

doi:10.1109/zinc.2019.8769404

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Cosic et al [4] propose an ADAS algorithm using video frames to calculate the Time to Potential Collision (TTC) from an oncoming vehicle's camera-installed external mirror. The algorithm tracks and detects vehicles, providing TTC data based on distance and speed.…”

Section: Datamining Based Modelsmentioning

confidence: 99%

“…Several publications in the literature review addressed the integration of ML, DL, and DM approaches into the design of ADAS to enhance road safety and reduce accidents with respect to Research Question 2. For instance, Cosic et al's [4] algorithm with ADAS uses video frames from an outside rear-view mirror camera to estimate the time to potential collision (TTC) for a vehicle coming from behind. The programme uses vehicle detection and tracking to determine the approaching vehicle's distance and speed and provide data about the TTC.…”

Section: Observationsmentioning

confidence: 99%

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Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

Manchala,

Kishore

2024

PJBAS

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The Internet of Vehicles (IoV) has transfigured transportation with connected vehicles, smart infrastructure, and self-driving cars. Road collisions and accidents are still a problem for road safety. This review of the literature discusses the prediction of IoV accidents and collisions as well as the detection of hazards using data mining, deep learning, and machine learning techniques. It describes the most recent developments to these methods and how they enhanced IoV safety. The article starts off by going over data collection, data quality, and the ever-changing nature of IoV traffic scenarios. What follows is a detailed breakdown of the ML, DL, and DM methods used in IoV safety applications. Convolutional neural networks, artificial neural networks, recurrent neural networks, support vector machines, and decision trees. As examples of real-world applications and case studies, intelligent accident prediction models, driver attention forecasting, traffic congestion forecasting, spatiotemporal analysis in autonomous vehicles, scene-graph embedding, and V2P collision risk alerts are discussed. The goal of this review is to give readers a comprehensive overview of the cutting-edge methods enhancing IoV accident prediction, collision avoidance, and hazard detection.

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Section: Datamining Based Modelsmentioning

confidence: 99%

Section: Observationsmentioning

confidence: 99%

Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

Manchala,

Kishore

2024

PJBAS

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show abstract

“…e ADAS algorithm only uses video frames captured by cameras in the exterior rearview mirrors to estimate time-to-potential collision (TTC) with vehicles from behind [15]. Pyo et al propose a vehicle detection-based FCW system for highway environments with a CNN as the classifier and time-to-collision (TTC) as the collision warning index [16].…”

Section: Introductionmentioning

confidence: 99%

Forwarding Collision Assessment with the Localization Information Using the Machine Learning Method

Guo

Jia

et al. 2022

Journal of Advanced Transportation

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Freeway crashes occupied 30% of total crashes. The advanced driver-assistance system (ADAS) often underestimates or omits the necessary collision warning. To investigate the forward collision in complex traffic conditions, the TTC (time-to-collision) is regarded as a surrogate for collision risk assessment. The study aims to design a forward-collision warning method for ADAS in the urban freeway scenario. The testing vehicle is equipped with sensors and a satellite navigation system. The TTC was collected from the Xi’an Rao Cheng expressway for the car-following scenario for three days. A comprehensive Gaussian model, which consists of three sub-GMM models is applied to describe the TTC distribution. The vehicle trajectories were extracted in the car-following state. To improve the efficiency of the forwarding collision system, four seconds are chosen as an analysis window in the car-following state. Two time-series machine models were used to predict TTC in advance. The TTC prediction models were constructed on the basis of the long short-term memory (LSTM). The prediction result was compared with the deep belief network (DBN) model. The comparison results show that the LSTM model is faster than the DBN model. The number of iterations is 100. The loss function of the LSTM is 0.06. The LSTM outperforms the DBN model and is suitable for car-following warnings.

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“…It helps to see the area behind the car when backing up. ADAS algorithms estimates the time to potential collision (TTC) with the vehicle coming from behind, which can be seen by the driver using rear-view camera [2]. ALPR is still an open problem because of the huge diversity in image Acquisition state (lightning condition, capturing angle, distance from camera, clarity and quality of image in terms of resolution of pixels) and Number plate (NP) format, which differs for different countries [3].…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Real time Embedded system for Rear view camera Video signal processing for Driving safety

Chandana¹

2022

IJRASET

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The use of vehicles in our life is increasing exponentially day by day and as increasing vehicles are violating the traffic rules, theft of vehicles, entering in restricted areas, high number of accidents lead to increase in the crime rates linearly. For any vehicle to be recognized, vehicle license plate detection will play a major significant role in this active world. For finding vehicles commonly used in field of security and safety system, LPDR plays a significant role and need to recognize vehicles registration number. The system includes a camera connected to Raspberry Pi which captures the video of moving vehicles. The number of the following vehicle is displayed and the vehicle image were viewed on a web page. The system implemented can be used to improve road safety.

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Time to collision estimation for vehicles coming from behind using in-vehicle camera

Cited by 6 publications

References 5 publications

Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

Forwarding Collision Assessment with the Localization Information Using the Machine Learning Method

Design and Development of Real time Embedded system for Rear view camera Video signal processing for Driving safety

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