Modern multi-level indoor parking environments promise to alleviate the parking problems in modern cities but they are oftentimes stressful for human drivers. Increasing automation of the parking process has the potential for significant gains in efficiency, safety and comfort but requires highly accurate sensing and monitoring of the environment. Another challenge is the appropriate visualization of large amounts of sensor data from disparate sources, in an intuitively understandable way. We address these challenges with our platform VPIPE for realistic visualization of 3D parking environments, parking lots and sensor data of vehicles. As central building block for this platform, we propose a cost-effective camera-based parking lot monitoring system that uses a cascade of Random Forest and Artificial Neural Network classifiers. The achieved detection accuracy in our parking testbed is 94.98%

One of the key requirements for the evaluation of indoor localization systems is an accurate and reliable ground truth. Existing ground truth systems are often expensive due to high hardware cost and complex deployment. In this work, we present a simple yet highly accurate approach for a cost-effective ground truth system based on off-the-shelf infrastructure cameras and printable markers. We developed a marker detection algorithm and systematic 3-layer projection approach between multiple coordinate systems which achieves a median accuracy of 0.48cm, 0.05 degrees and a minimum accuracy of 0.75cm, 0.27 degrees for 2D position and orientation

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Today's Advanced Driver Assistance Systems (ADAS) adopt an autonomous approach with all instrumentation and intelligence on board of one vehicle. In order to further enhance their benefit, ADAS need to cooperate in the future. This enables, for instance, to solve hazardous situations by coordinated maneuvers for safety intervention on multiple vehicles at the same point in time. Our prototyping environment presented in previous work addresses developing such cooperative ADAS. Its underlying approach is to either bring ideas for cooperative ADAS through the prototyping stage towards plausible candidates for further development, or to discard them as quickly as possible. This is enabled by an iterative process of refining and assessment. In this paper, we focus on handling the application specific parameter space, and more precisely on the scenario related aspects. As a part of our iterative prototyping process, defining and tuning scenarios and application parameters are highly repetitive tasks which needs to be designed very efficiently. We, therefore, strive to create a scenario definition methodology, which provides best flexibility and a minimal expenditure of time on the developer side.