Evaluation of Video-Based Driver Assistance Systems with Sensor Data Fusion by Using Virtual Test Driving

info:eu-repo/semantics/published

The transportation sector is being transformed by the arrival of autonomous driving and advanced driver assistance systems (ADAS). The correct function of many of these systems relies heavily on detecting traffic signs which can be a life saver. In this article, different theoretical approaches to the detection and recognition of traffic signs are addressed. It covers the classical image processing methods, machine learning-based methods, and the state-of-the-art deep learning models. It also documents the difficulties which are encountered by systems in real world and gives the solution to increase the system potential to make it more robust and accurate. Keywords: traffic signs, driver assistance systems, technique, equalization.

Positioning, as one of the prime components of context, has been a driving factor in the development of ubiquitous computing applications throughout the past two decades. Based on the redundant positioning architecture, this paper discusses the issues of suitability and scalability of algorithms for sensor data fusion from analytical and practical perspectives.

This paper presents a platform for evaluating sensor data fusion algorithms based on a Network-on-Chip (NoC). The NoC is simulated by MPSoCSim that contains an ARM and several MicroBlaze processors. The approach is evaluated in an automotive context. Charging stations for electric vehicles consist of expensive sensors, as the energy measurement system is an important part used for billing purposes, for safety of the charging process and for security. The sensors, that are usually used, are special made solutions, which are thermally stable and highly accurate. The NoC infrastructure presented in this paper results in a new method for sensor data fusion, which decreases costs by using inexpensive sensor units. The target of the sensor data fusion approach is to replace expensive sensors with many inexpensive one without loosing accuracy. The presented simulation platform allows an easy exchange of sensor models and sensor data fusion algorithms. Complex algorithms can furthermore be transferred to programmable FPGA logic, which allows the simultaneous processing of sensor data.

A vision-based driver assistance system using image processing technology consists of three main modules: road detection, traffic sign detection/identification, obstacle detection. Traffic Sign Recognition is an upcoming research field under applied Computer Vision and Machine Learning which deals with the automatic detection and classification of traffic signs in traffic scene images. In the current paper, we discuss the methodology used to detect the road sign from a traffic sign scene image and present a technique that can be used to construct a system that recognizes road signs in images. Such a system will ensure that each driver is aware of the rules & hazards on the road & will thus help in improving road safety. Also, the location of the road sign in the traffic sign image is unknown. Once these obstacles are overcome, such a system could be integrated in a Driver Assistance System. The primary objective is to develop an algorithm, which will identify various types of road signs from streaming video of real time road scenes in a reasonable time frame. The algorithm has two main parts: the first one, detection which uses the color and shape information to detect a road sign in a traffic scene picture. The second one, prediction & classification based on Neural Network concept. Our algorithm proposes a variety of MATLAB Image Processing Toolbox commands to determine if a road sign is present in current image. If present, the sign is resized and certain features from the Region of Interest (RoI) are fed as a feature vector to a neural net. This trained Neural Classifier then predicts the class of the road sign. Genetic Algorithms (GA) with built-in intelligence could be incorporated into the current system which will try to look for traffic signs only in environments where there is probability of finding/detecting one.

To avoid road accidents and for safe driving, a proper traffic sign recognition and classification is necessary. It is also an important part of intelligent transport systems and driverless vehicles. This article outlines the different ways to recognize and classify traffic signs. Various types of algorithms are used to recognize text-based, shape-based and color-based traffic signs. With the development of Artificial Intelligence, deep learning models are becoming more popular. CNN models are used for both detection and classification. Self-driving cars must learn to obey road signs in order to drive safely and smoothly. A transfer learning-based approach to traffic sign recognition and classification is used to reduce data costs. This survey analyses different machine learning and deep learning based methods for traffic sign recognition. The traffic sign recognition and classifications are applicable in intelligent transport system, driving autonomous vehicle, conducting road surveys, improving road safety and infrastructure.

Wireless capsule endoscope (WCE) offers a noninvasive investigation of the entire small intestine, which other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to keep track of the 3D trajectory that the capsule has traveled inside the lower abdomen to identify the positions of the intestinal abnormalities after they are found by the video source. However, existing commercially available radio frequency (RF)-based localization systems can only provide inaccurate and discontinuous position estimation of the WCE due to nonhomogeneity of body tissues and highly complicated distribution of the intestinal tube. In this paper, we present a hybrid localization technique, which takes advantage of data fusion of multiple sensors inside the WCE, to enhance the positioning accuracy and construct the 3-D trajectory of the WCE. The proposed hybrid technique extracts motion information of the WCE from the image sequences captured by the capsule's embedded visual sensor and combines it with the RF signal emitted by the wireless capsule, to simultaneously localize the WCE and mapping the path traveled by the WCE. Experimental results show that the proposed hybrid algorithm is able to reduce the average localization error from 6.8 cm to <;2.3 cm of the existing RF localization systems and a 3-D map can be precisely constructed to represent the position of the WCE inside small intestine.

Locating roadworks sites via detecting change in lateral positions of traffic signs measured relative to the ego-car

Multi sensor data fusion plays a significant role in addressing the research problems for mobile robot navigation. In this paper, a fusion procedure was developed, based on an Adaptive Neuro Fuzzy Inference Systems (ANFIS), which fuses the data from ultrasonic and LiDAR sensors for a better range estimation as well as environment perception. The root mean square error analysis between the measured and actual distance across different experiment trials indicates that, ultrasonic sensor could provide the data with a root mean square error (RMSE) which varies between 0.3118 cm and 4.9953 cm, whereas the LiDAR sensor could provide the same between 5.1503 cm and 10.4773 cm over 0–4 m range. The RMSE of the proposed fusion algorithm varies between 3.4831 cm and 6.1471 cm. It can be observed that, the fusion process could reduce the mean square error present with the high cost LiDAR sensing system by one-half, while fusing it with the low cost ultrasonic sensing system. The fusion algorithm discussed in this paper provides a guidance to define various operating/safety zones for initiating necessary control action during the navigation of the mobile robots.

In this paper we describe traffic sign recognition with neural networks in the frequency domain. Traffic signs exist in all countries to regulate the traffic of vehicles and pedestrians. Each country has its own set of traffic signs that are more or less similar. They consist of a set of abstract forms, symbols, numbers and letters, which are combined into different signs. Automatic traffic sign recognition is important for driver assistance systems and for autonomous driving. Traffic sign recognition is a subtype of image recognition. The traffic signs are usually recorded by a camera and must be recognized in real time, i.e. assigned to a class. We use neural networks for traffic sign recognition. The special feature of our method is that the traffic sign recognition does not take place in the spatial domain but in the frequency domain. This has advantages because it is possible to significantly reduce the number of neurons and thus the computing effort of the neural network compared to a conventional neural network.

Worldwide, traffic safety is a strong concern as traffic accidents are one of the leading causes of death. In this context, advanced driver assistance systems (ADAS) and autonomous vehicles are traffic management measures aimed at improving road safety and flow. Automatic detection and recognition of traffic signs are important for intelligent vehicles and ADAS systems. This work proposes a pipeline of digital image processing (DIP) techniques, machine learning (ML), and temporal coherence to perform the detection and recognition of Brazilian traffic signs in videos aiming an application for real-time systems to help in traffic safety and to reduce the number of fatal accidents. We are mainly interested in recognizing signs of speed limit group, no overtaking and obligatory passage, thus our detection considers the traffic sign with a circular shape and red border. For detection, the red color segmentation and the Hough transform are used to find circular regions that will be classified through the SVM algorithm in sign and not sign. For recognition of these signs, the support vector machines (SVM) are used. For speed limit signs the thresholding and contours are used to segment the digits for later classification. Our proposed method achieved an accuracy of 0.82 in detection, an increase of 18% in the number of recognized frames and 0.96 in the recognition stage using temporal coherence.

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

An accident is the cry of a lifetime. The role of traffic signs is most important in preventing or mitigating accidents. In many cases, while driving, traffic signs are not visible in the conventional approach; if traffic sign detection and recognition can alert the driver of important sign guidance ahead, it will help in accident mitigation. Traffic sign recognition plays a monumental role in expert systems, such as traffic assistance driving systems and automatic driving systems. The prime purpose of this paper is to design and identify a computer-based system that can spontaneously detect the direction of a road sign. For this research work, we have created our own dataset, which is called the Bangladeshi Traffic Sign Recognition Benchmark (BTSRB) dataset. The dataset, BTSRB, was created by capturing images from different angles and under different parameters and conditions. A total of 7320 images were collected to create this comprehensive database. This dataset called BTSRB all the images collected from Bangladesh. In this paper, we used five different types of models (CNN, Inception V3, MobileNetV2, ResNet50, and VGG16), which are pre-trained on the ImageNet dataset. Later, we finetuned the pre-trained model and used transfer learning. The main challenge of this research is collecting datasets from a country like Bangladesh, where no recognized dataset is available. When compared to another model, the accuracy of this model is greater than 91%. This paper emphasizes the significance of traffic sign recognition in expert systems and the necessity for a well-established dataset in nations where such resources are not readily available.

The traffic sign detection and recognition is necessary for the safety and proper navigation of drivers. Intelligent driver assistance systems have great potential in emerging technologies. This paper presents an efficient algorithm which detects the traffic sign from video based on colour and shape information. Then the auto associative neural networks are performed to recognise the traffic signs. This paper concentrates on red colour traffic signs. The algorithm is robust and able to detect the traffic signs with complex and randomly changing natural scenes. The algorithm is trained to find the best architecture for recognition. The experimental results shows the accuracy of detection and recognition of traffic signs.