Tim Tiedemann

To enable a safe and reliable operation in future urban air mobility scenarios with a large number of autonomous aerial vehicles, automatic sensing systems will be required during the starting and landing phase as well as through comparatively narrow air spaces typical for urban areas. This paper shows an implementation of a compressive sensing (CS) based obstacle-detection and direction-of-arrival (DoA) estimation approach. To this end, a multiple-input multiple-output frequency-modulated continuous wave radar system with two transmit and four receive antenna elements is employed to sense the environment and provide situational awareness from a virtual aperture with eight antennas. Example measurements result from a typical urban air setting with a-priori unknown sparsity. The results show that greater signal dynamics are attained from CS compared to conventional beamforming based DoA estimation techniques. Furthermore, measurements are verified by corresponding time-synchronous lidar measurements. CS-based obstacle detection and DoA estimation is shown to work even in complex measurement environments.

Artificial Intelligence (AI) methods need to be evaluated thoroughly to ensure reliable behavior. In applications like autonomous driving, a complex environment with an uncountable number of different situations and conditions needs to be handled by a method whose behavior needs to be predictable. To accomplish this, simulations can be used as a first step. However, the physical world behaves differently, as the example of autonomous driving shows. There, erroneous behavior has been found in test drives that was not noticed in simulations. Errors were caused by conditions or situations that were not covered by the simulations (e.g., specific lighting conditions or other vehicle's behavior). However, the problem with real world testing of autonomous driving features is that critical conditions or situations occur very rarely—while the test effort is high. A solution can be the combination of physical world tests and simulations—and miniature vehicles as an intermediate step between both. With model cars (in a sufficiently complex model environment) advantages of both can be combined: (1) low test effort and a repeatable variation of conditions/situations as an advantage like in simulations and (2) (limited) physical world testing with unspecified and potentially unknown properties as an advantage like in real-world tests. Additionally, such physical tests can be carried out in less stable cases like already in the early stages of AI method testing and/or in approaches using online learning. Now, we propose to use a) miniature vehicles at a small scale of 1:87 and b) use sensors and computational power only on the vehicle itself. By this limitation, a further consequence is expected: Here, autonomy methods need to be optimized drastically or even redesigned from scratch. The resulting methods are supposed to be less complex—and, thus, again less error-prone. We call this approach “Miniature Autonomy” and apply it to the road, water, and aerial vehicles. In this article, we briefly describe a small test area we built (3 sqm.), a large test area used alternatively (1,545 sqm.), two last generation autonomous miniature vehicles (one road, one aerial vehicle), and an autonomous driving demo case demonstrating the application.