An Improved Approach for Real-Time Taillight Intention Detection by Intelligent Vehicles

Tong, Bingming; Chen, Wei; Li, Changzhen; Du, Luyao; Xiao, Zhi; Zhang, Donghua

doi:10.3390/machines10080626

Cited by 14 publications

(5 citation statements)

References 38 publications

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“…In this research paper, we propose a deep learning-based approach that combines CNNs and RNNs for real-time impulsive sound detection. The proposed approach uses Mel Frequency Cepstral Coefficients (MFCCs) features extracted from audio signals and combines deep CNNs and RNNs to classify the features [23][24][25]. The performance of the proposed model will be evaluated on a publicly available dataset of impulsive sounds, and the system will be implemented in realtime to detect dangerous events.…”

Section: Ngo Et Al Proposed a Deep Cnn-rnn-based Approach Formentioning

confidence: 99%

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Смайлов¹,

Dosbayev²,

Omarov³

et al. 2023

IJACSA

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In this research paper, we presented a novel approach to detect impulsive sounds in real-time using a combination of Deep CNN and RNN architectures. The proposed approach was evaluated using our collected dataset of impulsive sounds, and the results showed that it outperformed traditional audio signal processing methods in terms of accuracy and F1score. The proposed approach has several advantages over traditional methods, including the ability to handle complex audio patterns, detect impulsive sounds in real-time, and improve its performance with a large dataset of labeled impulsive sounds. However, there are some limitations to the proposed approach, including the requirement for a large amount of labeled data to train effectively, environmental factors that may impact the accuracy of the detection, and high computational requirements. Overall, the proposed approach demonstrates the effectiveness of using a combination of Deep CNN and RNN architectures for impulsive sound detection, with potential applications in various fields such as public safety, industrial settings, and home security systems. The proposed approach is a significant step towards developing automated systems for detecting dangerous events and improving public safety.

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Section: Ngo Et Al Proposed a Deep Cnn-rnn-based Approach Formentioning

confidence: 99%

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Смайлов¹,

Dosbayev²,

Omarov³

et al. 2023

IJACSA

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“…In addition, brake-lights detection is also conducted based on the ROI size of the rear-lights region [17] or the frequency domain [16]. A number of learning-based methods have been proposed for brake-lights detection [10,11,29,[37][38][39][40][41][42]. Some methods extract color features from the derived rear-lights region or vehicle region, and train a classifier that predicts braking conditions with the extracted features [10,11,29,38].…”

Section: Brake-lights Detectionmentioning

confidence: 99%

“…Some methods extract color features from the derived rear-lights region or vehicle region, and train a classifier that predicts braking conditions with the extracted features [10,11,29,38]. By fine-tuning pre-trained object detectors (i. e., YOLO [43,44], Mask R-CNN [45]), they recognize vehicle braking conditions directly from a given image [37,[39][40][41][42].…”

Section: Brake-lights Detectionmentioning

confidence: 99%

Robust visual detection of brake-lights in front for commercialized dashboard camera

Moon

Park

2023

PLoS ONE

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The collision avoidance system (CAS) is an essential system for safe driving that alerts the driver or automatically applies the brakes in an expected situation of a vehicle collision. To realize this, an autonomous system that can quickly and precisely detect brake-lights of preceding vehicle is essential and this should works well in various environments for safety reason. Our proposed vision algorithm solves these objectives focusing on simple color features rather than a learning algorithm with a high computational cost, since our target system is a real-time embedded device, i.e., forward-facing dashboard camera. However, the existing feature-based algorithms are vulnerable to the ambient noise (noise problem), and cannot be flexibly applied to various environments (applicability problem). Therefore, our method is divided into two stages: rear-lights region detection using gamma correction for noise problem, and brake-lights detection using HSV color space for applicability problem, respectively. (i) Rear-lights region detection: we confirm the presence of the vehicle in front and derive the rear-lights region, and used non-linear mapping of gamma correction to make the detected region robust to noise. (ii) Brake-lights detection: from the detected rear-lights region, we extract color features using the HSV color range so that we can classify brake on and off in various conditions. Experimental results show that our algorithm overcomes the noise problem and applicability problem in various environments.

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“…Chen et al [22], identified brake lights of vehicles in urban areas and highways with 79% accuracy using Nakagami-m distribution. Tong et al [23], proposed a real-time strategy to detect vehicle taillights by combining a YOLOv4 detector with a Feature Pyramid Network (FPN) based module. Also, they evaluated their system under various weather conditions on different roads by collecting a dataset consisting of 3316 images from the BDD100K collection.…”

Section: Computer Vision and Deep Learningmentioning

confidence: 99%

Nighttime Driver Behavior Prediction Using Taillight Signal Recognition via CNN-SVM Classifier

Bagheri,

Barshooi

2023

Preprint

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This paper aims to enhance the ability to predict nighttime driving behavior by identifying the taillights of both human-driven and autonomous vehicles. The proposed model incorporates a customized detector designed to accurately detect front-vehicle taillights on the road. At the beginning of the detector, a learnable pre-processing block is implemented, which extracts deep features from input images and calculates the data rarity for each feature. In the next step, drawing inspiration from soft attention, a weighted binary mask is designed that guides the model to focus more on predetermined regions. This research utilizes Convolutional Neural Networks (CNNs) to extract distinguishing characteristics from these areas, then reduces dimensions using Principal Component Analysis (PCA). Finally, the Support Vector Machine (SVM) is used to predict the behavior of the vehicles. To train and evaluate the model, a large-scale dataset is collected from two types of dash-cams and Insta360 cameras from the rear view of Ford Motor Company vehicles. This dataset includes over 12K frames captured during both daytime and nighttime hours. To address the limited nighttime data, a unique pixel-wise image processing technique is implemented to convert daytime images into realistic night images. The findings from the experiments demonstrate that the proposed methodology can accurately categorize vehicle behavior with 92.14% accuracy, 97.38% specificity, 92.09% sensitivity, 92.10% F1-measure, and 0.895 Cohen’s Kappa Statistic. Further details are available at https://github.com/DeepCar/Taillight_Recognition.

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An Improved Approach for Real-Time Taillight Intention Detection by Intelligent Vehicles

Cited by 14 publications

References 38 publications

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Robust visual detection of brake-lights in front for commercialized dashboard camera

Nighttime Driver Behavior Prediction Using Taillight Signal Recognition via CNN-SVM Classifier

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