Detection Systems for Airbag Deployment

The airbag enablement system in today’s automobiles is not ideal and may allow airbags to deploy when they should not. Weight sensors detect pressure when someone sits in a passenger seat, enabling the airbag to deploy if an accident occurs. This system is flawed. For example, if a heavy box is placed in a passenger seat, the airbag will be unnecessarily enabled. The goal of this research project was to determine if different sensors—not weight sensors or cameras—could be used to identify the occupant of an automobile seat. Using a microchip programmed in C language, a circuit was designed with three sensors: ultrasonic range, passive infrared, and temperature. The temperature sensor was placed on an automobile seat to detect heat from occupant of the chair. The passive infrared sensor was positioned in front and above the seat to detect heat movement, and in order to detect the height of the occupant, the ultrasonic sensor was placed above the seat. The sensors can conclusively determine whether the occupant is living or inanimate. The sensors cannot always determine if the occupant is specifically human or the age of the human

Exploitation of time-of-flight (ToF) cameras

This technical report reviews the state-of-the art in the field of ToF cameras, their advantages, their limitations, and their present-day applications sometimes in combination with other sensors. Even though ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight and reduced power consumption have motivated their use in numerous areas of research. In robotics, these areas range from mobile robot navigation and map building to vision-based human motion capture and gesture recognition, showing particularly a great potential in object modeling and recognition.Preprin

Single-imager occupant detection based on surface reconstruction

This thesis introduces a novel framework for a real-time occupant detection system capable of extracting both two- and three-dimensional information using a single imager with active illumination. The primary objective of this thesis is to demonstrate the feasibility of such a low-cost classification system with comparable performance to multi-camera based stereo vision systems. Severe illumination conditions characterised by a frequent and wide illumination fluctuation are also challenging problems addressed in this work. The proposed system is designed to solve a problem of classifying three occupant classes being an adult, a forward-facing child seat, and a rear-facing child seat. DoubleFlash is employed to eliminate the influence of ambient illumination and to compress the optical dynamic range of target scenes. The idea underlying this technique is to subtract images flashed by different illumination power levels. The extension of this active illumination technique leads to the development of a novel shadow removal technique, called ShadowFlash. By simulating an artificial infinite illuminating plane over the field of view, the technique produces a shadowless scene without losing image details by composing multiple images illuminated from different directions. The ShadowFlash technique is then extended to the temporal domain by employing the sliding n-tuple strategy, which is introduced to avoid the reduction of the original frame rate. A modified active contour model, facilitated by morphological operations, extracts the boundary of the target object from the shadow-free scenes produced by the ShadowFlash. Based on the brightness information of the image triplet generated by the DoubleFlash, the orientations of the object surface at pixel points are estimated by the photometric stereo method and integrated into the 3D surface by means of global minimisation. The boundary information is used to specify the region of interest to reconstruct. Investigating both the two- and three-dimensional properties of vehicle occupants, 29 features are defined for the training of a neural network. The system is tested on a database of over 84,000 frames collected from a wide range of objects in various illumination conditions. A classification accuracy of 98.9% was achieved within the decision-time limit of three seconds

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered

Sensor Technologies for Intelligent Transportation Systems

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

2000 Transportation Scholars Conference: Compendium of Papers, 2000

Compendium of papers presented at the Transportation Scholars Conference in 2000

Advanced Air Bag Technology Assessment

As a result of the concern for the growing number of air-bag-induced injuries and fatalities, the administrators of the National Highway Traffic Safety Administration (NHTSA) and the National Aeronautics and Space Administration (NASA) agreed to a cooperative effort that "leverages NHTSA's expertise in motor vehicle safety restraint systems and biomechanics with NASAs position as one of the leaders in advanced technology development... to enable the state of air bag safety technology to advance at a faster pace..." They signed a NASA/NHTSA memorandum of understanding for NASA to "evaluate air bag to assess advanced air bag performance, establish the technological potential for improved technology (smart) air bag systems, and identify key expertise and technology within the agency (i.e., NASA) that can potentially contribute significantly to the improved effectiveness of air bags." NASA is committed to contributing to NHTSAs effort to: (1) understand and define critical parameters affecting air bag performance; (2) systematically assess air bag technology state of the art and its future potential; and (3) identify new concepts for air bag systems. The Jet Propulsion Laboratory (JPL) was selected by NASA to respond to the memorandum of understanding by conducting an advanced air bag technology assessment. JPL analyzed the nature of the need for occupant restraint, how air bags operate alone and with safety belts to provide restraint, and the potential hazards introduced by the technology. This analysis yielded a set of critical parameters for restraint systems. The researchers examined data on the performance of current air bag technology, and searched for and assessed how new technologies could reduce the hazards introduced by air bags while providing the restraint protection that is their primary purpose. The critical parameters which were derived are: (1) the crash severity; (2) the use of seat belts; (3) the physical characteristics of the occupants; (4) the proximity of the occupants to the airbag module; (5) the deployment time, which includes the time to sense the need for deployment, the inflator response parameters, the air bag response, and the reliability of the air bag. The requirements for an advanced air bag technology is discussed. These requirements includes that the system use information related to: (1) the crash severity; (2) the status of belt usage; (3) the occupant category; and (4) the proximity to the air bag to adjust air bag deployment. The parameters for the response of the air bag are: (1) deployment time; (2) inflator parameters; and (3) air bag response and reliability. The state of occupant protection advanced technology is reviewed. This review includes: the current safety restraint systems, and advanced technology characteristics. These characteristics are summarized in a table, which has information regarding the technology item, the potential, and an date of expected utilization. The use of technology and expertise at NASA centers is discussed. NASA expertise relating to sensors, computing, simulation, propellants, propulsion, inflatable systems, systems analysis and engineering is considered most useful. Specific NASA technology developments, which were included in the study are: (1) a capacitive detector; (2) stereoscopic vision system; (3) improved crash sensors; (4) the use of the acoustic signature of the crash to determine crash severity; and (5) the use of radar antenna for pre-crash sensing. Information relating to injury risk assessment is included, as is a summary of the areas of the technology which requires further development