Avionics architecture studies for the entry research vehicle

This report is the culmination of a year-long investigation of the avionics architecture for NASA's Entry Research Vehicle (ERV). The Entry Research Vehicle is conceived to be an unmanned, autonomous spacecraft to be deployed from the Shuttle. It will perform various aerodynamic and propulsive maneuvers in orbit and land at Edwards AFB after a 5 to 10 hour mission. The design and analysis of the vehicle's avionics architecture are detailed here. The architecture consists of a central triply redundant ultra-reliable fault tolerant processor attached to three replicated and distributed MIL-STD-1553 buses for input and output. The reliability analysis is detailed here. The architecture was found to be sufficiently reliable for the ERV mission plan

Car-Snow Clearing Drone

The purpose of this MQP was to research, design, analyze, and test a robotic device to remove snow and ice off of cars. Snow on cars is a hazard for many drivers across the world. Drones are widely used in society, including agricultural drones for spraying pesticides on crops. Lightweight, yet strong, carbon fiber was used in addition to 3-D printed parts. The flying and spraying components were each tested in addition to proving the concept with multiple FEA simulations on individual components. Computer vision was used to identify how much snow remained on the car and what areas were already sprayed. The primary focus of the project was to come up with a device to aid society and potentially sell to consumers

Where Truck Drivers Stop – Application of Vehicle Tracking Data for the Identification of Rest Locations and Driving Patterns

Road transport plays an essential role in freight transport throughout Europe, therefore, conditions that may hinder seamless operations in this sector require thorough consideration for evidence-based action. Critical amongst these key conditions is how, when, and where truck drivers stop, as a common set of rules strictly regulates their driving times and rest periods, which causes mandatory interruptions in the supply chains. However, approximating reliable estimations of freight traffic flows and road infrastructure usage constitutes a considerable challenge for researchers. This paper presents a robust data processing approach to designate rest area stops and to calculate the pertaining driving and rest times. Drawing on the abundance of navigation information provided by private fleet toll registration services, a comprehensive spatial-temporal truck stop database on all major rest areas along the toll road network in Hungary has been compiled. Based on the assessment and comparison of driving and rest times, driving and parking times have been analysed, including micro-scale analysis of particular rest areas. Both the methods applied and the results achieved can be of strategic interest to better understand truck driving patterns, as well as to develop targeted and cost-effective measures to streamline freight transport operations in other contexts

Are You Responsible for Traffic Congestion? A Systematic Review of the Socio-technical Perspective of Smart Mobility Services

A large amount of the pollution of modern cities is caused by individual transportation. Hence, many road users suffer from stress, emissions and noise. Smart mobility services can help improving the situa-tion by distributing traffic more consistently across different routes, times, and transportation modes. These services comprise two dimensions, a technical and a socio-technical. The latter addresses the road user’s role as data and knowledge provider and stresses the road user’s role in actively contributing to relieved traffic. As such, road users display one of the strongest levers to sustainably relieve traffic both in terms of knowledge providers and traffic actors. Using a systematic analysis of 28 publications, we show that existing SMob services show several chal-lenges related to the involvement of road users. We call for more research on SMob services that account for long-term user involvement e.g. by positively in-fluences road users’ practices and routines

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd