Objective: one standardised solution for all driving manoeuvres
This manoeuvre and many more like it are the result of the industrial mathematicians’ hard work: they closely analyse the situations and driving manoeuvres that can arise in traffic, recreate them in non-linear functions, then devise an algorithm that enables the system to interpret these situations and determine the optimum response. “As mathematicians, it is our job to abstract situations,” explains Büskens. The ultimate aim is to develop one standardised solution that can be deployed for each and every driving manoeuvre.
Assistance systems have up to now tackled one problem at a time
The AO Car project has highlighted one issue with current technology: the assistance systems that are already integrated into some cars, which perform certain actions automatically, are stand-alone solutions. They have been developed completely separately from one another and rely upon different software and problem solving models. This poses a problem when it comes to connecting them into a single system that could replace the driver. Though news reports and television advertisements often seem to suggest that we will be seeing self-driving cars on our streets any day now, Professor Büskens offers a more realistic assessment of the latest developments: “Complete solutions exist for individual scenarios, but not for the complex traffic environment we see in our towns and cities.” What a driver has to achieve in terms of perception, action and learning is highly complex.
Identifying parking spaces
Example: parking in a space. Assistance systems can already park a vehicle but, as Sommer explains, “the car not only needs to be able to park itself, but it also has to locate the parking space when it drives into a car park”. It needs to explore the surroundings and correctly identify a parking space, while also accounting for possible obstacles such as pedestrians, bollards or shopping trolleys. As a first step, the Bremen team wants to have fully developed a solution for this scenario by the end of the year.
Applying principles of space travel
There are three working groups contributing to the project at the University of Bremen. Alongside Büskens’ working groups for Optimisation and Optimal Control and for Computer Graphics and Virtual Reality, the working group for Cognitive Neuroinformatics is fine-tuning the sensor fusion technology and working on the decision making processes. The project team has also partnered with the Institute of Space Technology and Space Applications from the University of the German Federal Armed Forces in Munich. Just like Büskens’ team, they contribute valuable expertise from space travel projects, because “a lot of it transfers over” according to Matthias Rick. Whether sensors are on a distant planet or on a car on the street, the requirements are much the same: the vehicle needs to safely manoeuvre from A to B and therefore has to overcome known and unknown variables.
Laura Sommer and Matthias Rick have completed over 100 test drives so far.
© WFB / Focke Strangmann