Speaker
Description
In this talk, I will present challenges that arise in an industrial setting.
1) the planning of traffic, in particular in urban areas, map matching, and optimal planing of an autonomous drive.
The major part of the talk will be on the first scenario, where we the main tension is between individual goals (often: quickest path) and global ones (reduce overall emissions in a city). This turns very quickly into a high-dimensional, multi-objective problem where machine learning can be one approach to solve it.
2) Map matching of noisy positions to a network of roads. Traditionally, this is solved by Hidden Markov Models, where the goal is to determine the optimal path on a map with positions that are noisy.
3) Deployment of AI software on hardware with restrictions (memory, cpu, scheduling involving other processes, safety, security). This will be a minor part of the talk and shall illustrate the potential that optimization and machine learning have in real-world applications. Typically packaging is done automated, for most applications that means, however, that restrictions on hardware are not considered from the beginning. The design of a feedback loop on development and efficient packaging is an example how problems may arise at places one does not think from the beginning.
