Autonomous

Everything You Need to Know About Autonomous Driving / Self-driving / Driverless / Robot Cars. Functionality To Impact.

Drones and Autonomous Cars Building, Control and Simulation | Electric Mobility Technology and Future

share their practical experiences designing autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions as to its future actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, new algorithms can be tested so as to update the HD map—in addition to training better recognition, tracking, and decision models.

This book treats the theory of pullback attractors for non-autonomous dynamical systems. While the emphasis is on infinite-dimensional systems, the results are also applied to a variety of finite-dimensional examples.

Autonomous sensors transmit data and power their electronics without using cables. They can be found in e.g. wireless sensor networks (WSNs) or remote acquisition systems. Although primary batteries provide a simple design for powering autonomous sensors, they present several limitations such as limited capacity and power density, and difficulty in predicting their condition and state of charge. An alternative is to extract energy from the ambient (energy harvesting). However, the reduced dimensions of most autonomous sensors lead to a low level of available power from the energy transducer. Thus, efficient methods and circuits to manage and gather the energy are a must.

Decades of innovations in combinatorial problem solving have produced better and more complex algorithms. These new methods are better since they can solve larger problems and address new application domains. They are also more complex which means that they are hard to reproduce and often harder to fine-tune to the peculiarities of a given problem. This last point has created a paradox where efficient tools are out of reach of practitioners.

This book constitutes the thoroughly refereed post-workshop proceedings of the 4th International Workshop on Modelling and Simulation for Autonomous Systems, MESAS 2017, held in Rome, Italy, , in October 2017.
The 33 revised full papers included in the volume were carefully reviewed and selected from 38 submissions. They are organized in the following topical sections: M&S of Intelligent Systems – AI, R&D and Applications; Autonomous Systems in Context of Future Warfare and Security – Concepts, Applications, Standards and Legislation; Future Challenges and Opportunities of Advanced M&S Technology.