Detection of arbitrarily rotated parked cars based on radar sensors

Lombacher, Jakob; Hahn, Markus; Dickmann, Jürgen; Wöhler, Christian

doi:10.1109/irs.2015.7226281

Cited by 16 publications

(3 citation statements)

References 12 publications

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“…Grid maps are divided into amplitude grid maps and the much more common occupancy grid maps [101], which represent the probabilistic cell states of the environment. This means occupancy grid maps allow for a distinction between free space, obstacles, and an unknown area, as shown in the example in Fig 22. In contrast to feature-based approaches, the free space and the occupied space is directly visible in such maps and does not have to be interpolated or calculated additionally [102], [103]. Due to the probabilistic cell representation, neither ghost targets are represented as real targets nor hidden targets as free space, which enables a highly accurate and very robust map.…”

Section: Grid Mapping and Automotive Sarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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Section: Grid Mapping and Automotive Sarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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“…RFID sensors can be used to detect the presence or absence of automobiles in parking places in the context of parking detection. RFID sensors work by emitting a radio signal that is picked up by an RFID tag attached to the vehicle [44]. The RFID tag contains a unique identification number that is transmitted back to the sensor.…”

Section: Radio Frequency Identification (Rfid)mentioning

confidence: 99%

A Review of Parking Slot Types and their Detection Techniques for Smart Cities

Kumar,

Singh,

Raja

et al. 2023

Smart Cities

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Smart parking system plays a critical role in the overall development of the cities. The capability to precisely detect an open parking space nearby is necessary for autonomous vehicle parking for smart cities. Finding parking spaces is a big issue in big cities. Many of the existing parking guidance systems use fixed IoT sensors or cameras that are unable to offer information from the perspective of the driver. Accurately locating parking spaces can be difficult since they come in a range of sizes and colors that are blocked by objects that seem different depending on the environmental lighting. There are numerous auto industry players engaged in the advanced testing of driverless cars. A vacant parking space must be found, and the car must be directed to park there in order for the operation to succeed. The machine learning-based algorithms created to locate parking spaces and techniques and methods utilizing dashcams and fish-eye cameras are reviewed in this study. In response to the increase in dashcams, neural network-based techniques are created for identifying open parking spaces in dashcam videos. The paper proposed the review of the existing parking slot types and their detection techniques. The review will highlight the importance and scope of a smart parking system for smart cities.

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“…In [7] and [8] it was shown how to use such methods for static radar data, i.e., non-moving objects such as buildings or parked cars. For dynamic data like moving road users a grid mapping procedure as in these works would require longer exposure times.…”

Section: Introductionmentioning

confidence: 99%

Radar-based Feature Design and Multiclass Classification for Road User Recognition

Scheiner

Appenrodt

Dickmann

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an -with respect to well established camera systems -orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different features are extracted from the measurement data and tested on their performance. From these features a suitable subset is chosen and passed to random forest and long short-term memory (LSTM) classifiers to obtain class predictions for the radar input. Moreover, it is shown why data imbalance is an inherent problem in automotive radar classification when the dataset is not sufficiently large. To overcome this issue, classifier binarization is used among other techniques in order to better account for underrepresented classes. A new method to couple the resulting probabilities is proposed and compared to others with great success. Final results show substantial improvements when compared to ordinary multiclass classification.

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Detection of arbitrarily rotated parked cars based on radar sensors

Cited by 16 publications

References 12 publications

Automotive Radar — From First Efforts to Future Systems

Automotive Radar — From First Efforts to Future Systems

A Review of Parking Slot Types and their Detection Techniques for Smart Cities

Radar-based Feature Design and Multiclass Classification for Road User Recognition

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