Tiedemann, Tim (German Research Center for Artificial Intelligence (DFKI)) | Voegele, Thomas (German Research Center for Artificial Intelligence (DFKI)) | Krell, Mario Michael (University of Bremen) | Metzen, Jan Hendrik (University of Bremen) | Kirchner, Frank (German Research Center for Artificial Intelligence (DFKI) and University of Bremen)
In the presented research project, a software and hardware infrastructure for parking space focussed inter-modal route planning in a public pilot region in Berlin is developed. One central topic is the development of a prediction system which gives an estimated occupancy for the parking spaces in the pilot region for a given date and time in the future. Occupancy data will be collected online by roadside parking sensors developed within the project. The occupancy prediction will be implemented using “Neural Gas” machine learning in combination with a proposed method which uses data threads to improve the prediction quality. In this paper, a short overview of the whole research project is given. Furthermore, the concept of the software framework and the learning methods are presented and first collected data is shown. The prediction method using data threads is explained in more detail.
Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promising, indeed. However, existing approaches are limited by the high cost of sensors that need to be installed throughout the city in order to achieve an accurate estimation. This work investigates the extension of estimating parking information from areas equipped with sensors to areas where they are missing. To this end, the similarity between city neighborhoods is determined based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas.
The future is all about self-parking cars. For anyone still traumatized from learning how to parallel park, a new patent from Bay Area-based electric vehicle company SF Motors will soothe your bumper-filled nightmares. The company's approved patent for design and methodology for a self-parking system takes sensors, cameras, and LIDAR (a laser system to measure distance between objects) to supply data to a computer that can direct the car on how to park. The system with sensors is mounted on the car at bumper level for prime parking data collection. A look at the patent application approved last month shows how the sensors tell the parking system about objects that may exist as well as how far away they are.
The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. While the deployment of sensor technologies continues to be core to the development of smart parking, a wide variety of other technology innovations are also enabling more adaptable systems--including cameras, wireless communications, data analytics, induction loops, smart parking meters, and advanced algorithms. The future of the smart parking market is expected to be significantly influenced by the arrival of automated vehicles (AVs). Several cities around the world are already beginning to trial self-parking vehicles, specialized AV parking lots, and robotic parking valets. For example, in Boulder, Colorado, ParkPlus is working on deploying the first fully automated parking garage in the Western United States through Boulder's PearlWest mixed-use development.
Finding on-street parking in congested urban areas is a challenging chore that most drivers worldwide dislike. Previousvehicle traffic studies have estimated that around thirty percent of vehicles travelling in inner city areas are made up ofdrivers searching for a vacant parking space. While there arehardware sensor based solutions to monitor on-street parking occupancy in real-time, instrumenting and maintainingsuch a city wide system is a substantial investment. In this paper, a novel vehicle parking activity detection method, calledParkUs, is introduced and tested with the aim to eventuallyreduce vacant car parking space search times. The systemutilises accelerometer and magnetometer sensors found in allsmartphones in order to detect parking activity within a cityenvironment. Moreover, it uses a novel sensor fusion featurecalled the Orthogonality Error Estimate (OEE). We show thatthe OEE is an excellent indicator as it’s capable of detecting parking activities with high accuracy and low energy consumption. One of the envisioned applications of the ParkUssystem will be to provide all drivers with guidelines on wherethey are most likely to find vacant parking spaces within acity. Therefore, reducing the time required to find a vacantparking space and subsequently vehicle congestion and emissions within the city.