Automotive Cameras Research Articles

FF-HPINet: A Flipped Feature and Hierarchical Position Information Extraction Network for Lane Detection.

Effective lane detection technology plays an important role in the current autonomous driving system. Although deep learning models, with their intricate network designs, have proven highly capable of detecting lanes, there persist key areas requiring attention. Firstly, the symmetry inherent in visuals captured by forward-facing automotive cameras is an underexploited resource. Secondly, the vast potential of position information remains untapped, which can undermine detection precision. In response to these challenges, we propose FF-HPINet, a novel approach for lane detection. We introduce the Flipped Feature Extraction module, which models pixel pairwise relationships between the flipped feature and the original feature. This module allows us to capture symmetrical features and obtain high-level semantic feature maps from different receptive fields. Additionally, we design the Hierarchical Position Information Extraction module to meticulously mine the position information of the lanes, vastly improving target identification accuracy. Furthermore, the Deformable Context Extraction module is proposed to distill vital foreground elements and contextual nuances from the surrounding environment, yielding focused and contextually apt feature representations. Our approach achieves excellent performance with the F1 score of 97.00% on the TuSimple dataset and 76.84% on the CULane dataset.

An Integrated and Fully Automated Test Solution for Automotive Cameras of Ultrawide Angle of View

An Integrated and Fully Automated Test Solution for Automotive Cameras of Ultrawide Angle of View

Evaluation of image quality metrics designed for DRI tasks with automotive cameras

Driving assistance is increasingly used in new car models. Most driving assistance systems are based on automotive cameras and computer vision. Computer Vision, regardless of the underlying algorithms and technology, requires the images to have good image quality, defined according to the task. This notion of good image quality is still to be defined in the case of computer vision as it has very different criteria than human vision: humans have a better contrast detection ability than image chains. The aim of this article is to compare three different metrics designed for detection of objects with computer vision: the Contrast Detection Probability (CDP) [1, 2, 3, 4], the Contrast Signal to Noise Ratio (CSNR) [5] and the Frequency of Correct Resolution (FCR) [6]. For this purpose, the computer vision task of reading the characters on a license plate will be used as a benchmark. The objective is to check the correlation between the objective metric and the ability of a neural network to perform this task. Thus, a protocol to test these metrics and compare them to the output of the neural network has been designed and the pros and cons of each of these three metrics have been noted.

Color performance of 0.8 um CMOS image sensor with CMY color filters

Modern digital cameras capture images with a subsampling method via mosaic color filter array (CFA). Of particular interest is the CFA with Cyan-Magenta-Yellow (CMY) color filters. Despite the improvement of the sensitivity, images reconstructed from CMY CFA usually suffer from lower color fidelity compared to the conventional Red-Green-Blue (RGB) color filters. In this paper, we proposed a CMY CFA with novel spectral sensitivities (CMY2.0), which were carefully designed to overcome the shortcomings of previous CMY CFA (CMY1.0). A CMY CMOS image sensor (CIS) with such optimized spectral sensitivities is then realized in order to evaluate the color performance and signal-to-noise ratio (SNR). As a result, the camera equipped with the CMY CFA with proposed spectral sensitivities (CMY2.0) features both an improved sensitivity and a high color fidelity, which is suitable for a wide range of applications, such as low-light photography, under-screen cameras and automotive cameras.

Guest Editorial Introduction to the Special Issue on Solid-State Image Sensors

It is my sincere pleasure and honor to introduce the eighth Special Issue of the IEEE Transactions on Electron Devices on Solid-State Image Sensors. Previous special issues were published in February 1976, August 1985, May 1991, October 1997, January 2003, November 2009, and January 2016. In the six years since the last Special Issue, solid-state image sensors have continued to demonstrate impressive performance improvements and developments in a diverse set of technology areas. The areas include process modules, wafer-level stacking, single-photon counting, ultra-wide dynamic range, alternate detector materials, time-of-flight 3-D imaging, and device simulation. Solid-state image sensors also achieved continued growth in existing markets such as camera phones, automotive cameras, security and industrial cameras and medical/scientific cameras. New applications and markets, such as the Internet-of-Things, 3-D imaging, artificial intelligence, augmented reality, biometrics, and others, are driving further growth and technology development. Solid-state image sensors are now key components in a vast array of consumer and industrial products.

Application of spectral computing technics for color vision testing using virtual reality devices

Will autonomous vehicles ever be able to drive around safely? In Germany lane markers on streets are white. In construction zones temporary markers are installed on top of the standard ones that are yellow and they direct the traffic e.g. into shifted lanes. So, the only differentiation is the color. But what about the white markers at times close to the sunset? Then we drive into Austria and the temporary markers are turning orange. Or driving in the US makes the whole country a construction zone because markers are always yellow. Automotive cameras are often times not RGB cameras. They have other color filters to maximize sensitivity, which often times does not help differentiating colors. So does the spectral reflectance matter? Which impact does the illumination have? And there are many different ones like daylight at different times of the day, different kinds of streetlights, different headlights of the cars etc. Traffic signs create another color problem. We drive from Switzerland to France. In Switzerland the freeway signs are green and the major road signs are blue. When you cross the French border, it is the other way around. How can these problems be solved?

Long-Range, High-Resolution Camera Optical Design for Assisted and Autonomous Driving

High-quality cameras are fundamental sensors in assisted and autonomous driving. In particular, long-range forward-facing cameras can provide vital information about the road ahead, including detection and recognition of objects and early hazard warning. These automotive cameras should provide high-resolution images consistently under extreme operating conditions of the car for robust operation. This paper aims to introduce the design of fixed-focus, passively athermalized lenses for next-generation automotive cameras. After introducing an overview of essential and desirable features of automotive cameras and state-of-the-art, based on these features, two different camera designs that can achieve traffic sign recognition at 200 m distance are presented. These lenses are designed from scratch, with a unique design approach that starts with a graphical lens material selection tool and arrives at an optimized design with optical design software. Optical system analyses are performed to evaluate the lens designs. The lenses are shown to accomplish high contrast from − 40 °C to 100 °C and allow for a 4 × increase in resolution of automotive cameras.

Drowsiness Detection Based on the Analysis of Breathing Rate Obtained from Real-time Image Recognition

Road accidents and victims provoked by drowsiness are a worldwide relevant social and economic problem. In EU, 25% of road accidents are related with fatigue. The relation between drowsiness and accidents is supported by scientific evidences that relate microsleep and other fatigue episodes with the loss of control of the vehicle. Nowadays there exist different technological approaches for drowsiness mitigating while driving, which try to reduce these accidents by detecting driver's physical condition and acting on the driver response. The techniques for mitigating accidents are based on algorithms of detection of physiological parameters that ensure drivers confidence and an appropriate use of the technology. The most frequently used non-invasive system are the cameras, which have been explored and used, mainly detecting eye movement and eyelid closure (PERCLOS). These systems have limitations due to artifacts and noise related to environmental and emotional conditions, which might lead to false positives. With the purpose of solving these shortcomings, the goal of the research is to develop a system capable of detecting the level of drowsiness based on the involuntary movements of the driver provoked by the respiration captured by means of cameras. In the current research, robustness in front of different types of users and circumstances has been explored. A system of reduced size automotive cameras of high dynamics is proposed to be used as breathing rate sensor. Images captured by these sensors will be processed to obtain the driver's chest/abdomen movement. These data will be analyzed in real time by a validated algorithm that interprets the movement and obtains the level of fatigue and drowsiness of the driver. A twofold “gold standard” is used to compare the robustness of the developed system. An experimental design has been developed in order to take into consideration different anthropometric characteristics, clothing types, user and vehicle movement and light conditions. The result will provide the boundary conditions of any system based on on-board cameras. These data will be used for building the algorithms to detect and interpret breathing patterns. The results show the capabilities of this approach and also permit to define the needs and requirements of the resulting technological developments.

CAMERA-ON-A-CHIP: TECHNOLOGY TRANSFER FROM SATURN TO YOUR CELL PHONE

The invention of the CMOS active pixel image sensor “camera-on-a-chip” at the NASA Jet Propulsion Laboratory at Caltech and its subsequent development and commercialization via the spinoff company Photobit Corporation are discussed. The article traces the arc of the technology from innovation, technology transfer, and entrepreneurial startup to its use today in nearly all mobile phone cameras, tablet and web cameras, medical and automotive cameras, and many other applications.

Using Crowdsourcing to Establish the Big Data of the Intelligent Transportation System

The establishment of big data is completed based on the data captured by automotive electronic systems and cameras on the highway. This kind of crowdsourcing model for data gathering can not only accomplish the accurate collection of enormous data, but also solves the problem of high costs. After the collection of data, the isolated data is linked with each other and shared. In this way, the data will be socialized so as to establish the truly big data. With the big data collected, data center makes an analysis and mining to schedule, monitor and manage the transportation intelligently.

Focus on sensors and detectors

Focus on sensors and detectors