The applications of real-time machine learning on human mobility data continue to grow, thanks to the increase in digital mobility data, such as phone records, GPS traces, and social media posts. This has led to exponential growth in areas such as next location prediction, crowd flow, trajectory generation, flow generation, disease spreading, urban projection, and well-being.
Eclipse Muto provides an adaptive framework and a runtime platform for dynamically composable model-driven ROS software stacks on autonomous vehicles and robots in general
Our world is changing, and the change is visible everywhere. More than 50% of the population is living in cities and the cities are growing. More and more goods and people need to be transported, bringing the traffic infrastructure to its limits. Increasing pollution and noise levels bringing the environment to its limits.
To counter this, a transformation of the mobility is needed, who has already started, powered by new technologies and services. The mobility of the future will be electrified, connected, personalized, automated and software defined.
Despite being severly allergic to sunlight the average nerd in all of us sometimes needs to change her location. Of course she will never do so without simulating her path through the physical world in advance and predict possible disturbances on the road ahead. Furthermore, as so many of us are part of the "quantify yourself" movement aiming to optimize your life, the nerd is also keen to optimize the use of newly invented transport modes, like ridepooling, ridesharing or even podcars - but what is the best choice?
