Trailer Reverse Assist. Optical Follow Me

Backing-up a trailer is a difficult task even for experienced users, and thus, solutions exist for assisting the steering of a vehicle-trailer system by just requiring the input of the desired trailer’s trajectory. Nevertheless, the problem is not entirely solved as the selected trajectory clearance while reversing a trailer is not completely available due to visibility obstructions, making reversing a trailer an unsafe maneuver. The objective of this work is to perform a proof-of-concept of a system which aids the user in the process of backing up a trailer through the desired trajectory where limited visibility is present. This was accomplished by developing an add-on feature capable of tracking a helping person. The new feature provides the required information so that existing compatible trailer reversing solutions can steer and accelerate the vehicle to follow the tracked person while also keeping a safe distance. Moreover, potential collisions are prevented by the addition of a close proximity object detection functionality. For this, a scaled prototype of the proposed system was developed by applying the “Vee Model” methodology where the requirements, architecture, solution design, implementation, and validation steps were followed. A successful proof-of-concept was accomplished after validating the capacity of the prototype to both identify and follow a person, while maintaining a safe distance, and to detect objects in the vehicle’s path. In addition, the documentation of the system’s design, development, and validation was achieved rendering the feature ready for full scale development. In conclusion, the “Trailer Reverse Assist - Optical Follow Me” system add-on can further assist in the process of backing-up a trailer safely in environments where the visibility is limited while also preventing collisions with nearby objects.ITESO, A. C.ContinentalConsejo Nacional de Ciencia y Tecnologí

Control of autonomous multibody vehicles using artificial intelligence

The field of autonomous driving has been evolving rapidly within the last few years and a lot of research has been dedicated towards the control of autonomous vehicles, especially car-like ones. Due to the recent successes of artificial intelligence techniques, even more complex problems can be solved, such as the control of autonomous multibody vehicles. Multibody vehicles can accomplish transportation tasks in a faster and cheaper way compared to multiple individual mobile vehicles or robots. But even for a human, driving a truck-trailer is a challenging task. This is because of the complex structure of the vehicle and the maneuvers that it has to perform, such as reverse parking to a loading dock. In addition, the detailed technical solution for an autonomous truck is challenging and even though many single-domain solutions are available, e.g. for pathplanning, no holistic framework exists. Also, from the control point of view, designing such a controller is a high complexity problem, which makes it a widely used benchmark. In this thesis, a concept for a plurality of tasks is presented. In contrast to most of the existing literature, a holistic approach is developed which combines many stand-alone systems to one entire framework. The framework consists of a plurality of modules, such as modeling, pathplanning, training for neural networks, controlling, jack-knife avoidance, direction switching, simulation, visualization and testing. There are model-based and model-free control approaches and the system comprises various pathplanning methods and target types. It also accounts for noisy sensors and the simulation of whole environments. To achieve superior performance, several modules had to be developed, redesigned and interlinked with each other. A pathplanning module with multiple available methods optimizes the desired position by also providing an efficient implementation for trajectory following. Classical approaches, such as optimal control (LQR) and model predictive control (MPC) can safely control a truck with a given model. Machine learning based approaches, such as deep reinforcement learning, are designed, implemented, trained and tested successfully. Furthermore, the switching of the driving direction is enabled by continuous analysis of a cost function to avoid collisions and improve driving behavior. This thesis introduces a working system of all integrated modules. The system proposed can complete complex scenarios, including situations with buildings and partial trajectories. In thousands of simulations, the system using the LQR controller or the reinforcement learning agent had a success rate of >95 % in steering a truck with one trailer, even with added noise. For the development of autonomous vehicles, the implementation of AI at scale is important. This is why a digital twin of the truck-trailer is used to simulate the full system at a much higher speed than one can collect data in real life.Tesi

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented

PS Magazine 1953 Series Issue 015

PS Magazine, also known as the Preventive Maintenance Monthly, is an official publication of the Army, providing information for all soldiers assigned to combat and combat duties. The magazine covers issues concerning maintenance, maintenance procedures and supply problems.https://scholarscompass.vcu.edu/psm/1014/thumbnail.jp

Financing Invention During the Second Industrial Revolution: Cleveland, Ohio, 1870-1920

For those who think of Cleveland as a decaying rustbelt city, it may seem difficult to believe that this northern Ohio port was once a hotbed of high-tech startups, much like Silicon Valley today. During the late nineteenth and early twentieth centuries, Cleveland played a leading role in the development of a number of second-industrial-revolution industries, including electric light and power, steel, petroleum, chemicals, and automobiles. In an era when production and inventive activity were both increasingly capital-intensive, technologically creative individuals and firms required greater and greater amounts of funds to succeed. This paper explores how the city's leading inventors and technologically innovative firms obtained financing, and finds that formal institutions, such as banks and securities markets, played only a very limited role. Instead, most funding came from local investors who took long-term stakes in start-ups formed to exploit promising technological discoveries, often assuming managerial positions in these enterprises as well. Business people who were interested in investing in cutting-edge ventures needed help in deciding which inventors and ideas were most likely to yield economic returns, and we show how enterprises such as the Brush Electric Company served multiple functions for the inventors who flocked to work there. Not only did they provide forums for the exchange of ideas, but by assessing each other's discoveries, the members of these technological communities conveyed information to local businessmen about which inventions were most worthy of support.

Spartan Daily, March 23, 1990

Volume 94, Issue 40https://scholarworks.sjsu.edu/spartandaily/7970/thumbnail.jp

The Daily Texan

Texas Student Medi