| 94 | | In the near future, autonomous vehicles are expected to become a part of our daily lives. Secure, stable, and speedy vehicle network communication becomes one of the most important features to support this. At Kettering University, our team developed a vehicle network testbed in GM Mobility Research Center, which is a 22-acre vehicle test track in our campus. The vehicle testbed incorporates two types of wireless networks: 4G-LTE and DSRC (802.11p). Specifically, 4G-LTE is used for V2X application, while DSRC is used for V2V and V2I safety application. This testbed will be utilized on two major projects: |
| 95 | | |
| 96 | | SAE International and General Motors (GM) have partnered to headline sponsor |
| | 83 | In the near future, autonomous vehicles are expected to become a part of our daily lives. Secure, stable, and speedy vehicle network communication becomes one of the most important features to support this. At Kettering University, our team developed a vehicle network testbed in GM Mobility Research Center, which is a 22-acre vehicle test track in our campus. The vehicle testbed incorporates two types of wireless networks: 4G-LTE and DSRC (802.11p). Specifically, 4G-LTE is used for V2X application, while DSRC is used for V2V and V2I safety application. This testbed will be utilized on two major projects: !AutoDrive challenge and Smart Belt Coalition project. |
| | 84 | |
| | 85 | SAE International and General Motors (GM) have partnered to headline sponsor !AutoDrive Challenge, which is a three-year autonomous vehicle competition that will task students to develop and demonstrate a full autonomous driving passenger vehicle. The technical goal of the competition is to navigate an urban driving course in an automated driving mode as described by SAE Standard (J3016) level 4 definition by year three. As one of the eight participant team, our vehicle testbed will support Kettering Bulldog Bolt team to develop the best autonomous vehicle on Chevrolet Bolt EV. |
| 187 | | Current approaches to scientific research require time-consuming activities that do not advance our scientific understanding. For example, cleaning data and writing code to attempt to reproduce previously published research. Can we find a better way to create and publish workflows, data, and models? The Popper Experimentation Protocol (http://falsifiable.us) is a series of simple, easy-to-follow steps for implementing experiments using a |
| 188 | | |
| 189 | | Modern OSS development communities have created tools and practices (DevOps) to manage large codebases, allowing them to deal with high levels of complexity, not only in terms of code, but with the entire ecosystem that is needed in order to deliver changes to software in an agile, rapidly changing environment. Popper repurposes DevOps in the context of scientific explorations. |
| 190 | | |
| 191 | | We will illustrate how to make use of the Popper command-line tool in order to re-run an existing experiment using geni-lib to configure infrastructure. Subsequently, we will show how to make use of Ansible and Docker, as well as to implement post-analysis of results using Jupyter notebooks. Additionally, we will show how Popper can generate files that can be used to connect a |
| | 187 | Current approaches to scientific research require time-consuming activities that do not advance our scientific understanding. For example, cleaning data and writing code to attempt to reproduce previously published research. Can we find a better way to create and publish workflows, data, and models? The Popper Experimentation Protocol (http://falsifiable.us) is a series of simple, easy-to-follow steps for implementing experiments using a !DevOps approach. |
| | 188 | |
| | 189 | Modern OSS development communities have created tools and practices (!DevOps) to manage large codebases, allowing them to deal with high levels of complexity, not only in terms of code, but with the entire ecosystem that is needed in order to deliver changes to software in an agile, rapidly changing environment. Popper repurposes !DevOps in the context of scientific explorations. |
| | 190 | |
| | 191 | We will illustrate how to make use of the Popper command-line tool in order to re-run an existing experiment using geni-lib to configure infrastructure. Subsequently, we will show how to make use of Ansible and Docker, as well as to implement post-analysis of results using Jupyter notebooks. Additionally, we will show how Popper can generate files that can be used to connect a !GitHub project (a ""Popperized"" repo) with TravisCI to continuously validate experiments. |
