Motorway Measurement Campaign to Support R&amp;D Activities in the Field of Automated Driving Technologies

Tihanyi, Viktor; Tettamanti, Tamás; Csonthó, Mihály; Eichberger, Arno; Ficzere, Dániel; Gangel, Kálmán; Hormann, Leander B.; Klaffenböck, Maria Anneliese; Knauder, Christoph; Luley, Patrick Patrick; Magosi, Zoltán Ferenc; Magyar, Gábor; Németh, Huba; Soós, Gábor; Reckenzaun, Jakob; Remeli, Viktor; Rövid, András; Ruether, Matthias; Solmaz, Selim; Somogyi, Zoltán; Szántay, Dávid; Tomaschek, Tamás Attila; Varga, Pál; Vincze, Zsolt; Wellershaus, Christoph; Szalay, Zsolt

doi:10.20944/preprints202103.0184.v1

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…The measurement data are provided at the object level and include the corresponding RTK-GPS position data of all attending vehicles as ground-truth information. The considered measurement campaign took place in 2020 on a Hungarian motorway [43].…”

Section: Scope Of Workmentioning

confidence: 99%

“…As a dedicated activity of this exploratory phase, a measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners (see Figure 5). The measurement campaign generated ground-truth sensor data from both the vehicle and infrastructure perspectives, which proved to be extremely useful for future automotive research and development activities, in particular in the automated vehicle domain, due to the availability of the ground-truth information for static and dynamic content [43]. All the vehicles used in this testing campaign were equipped with high-accuracy differential Global Navigation Satellite Systems (GNSSs) for localization, each of which were calibrated for accurate positioning information.…”

Section: Campaign Descriptionmentioning

confidence: 99%

“…Different numbers of vehicles took part in various test drives depending on the test scenario. All of the test vehicles had calibrated high-accuracy GPSs, along with additional onboard sensors [43]. The setup of the test vehicle from Virtual Vehicle Research GmbH (VIF) is described in detail next.…”

Section: Campaign Descriptionmentioning

confidence: 99%

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Development and Experimental Validation of an Intelligent Camera Model for Automated Driving

Genser

Muckenhuber

Solmaz

et al. 2021

Sensors

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The virtual testing and validation of advanced driver assistance system and automated driving (ADAS/AD) functions require efficient and realistic perception sensor models. In particular, the limitations and measurement errors of real perception sensors need to be simulated realistically in order to generate useful sensor data for the ADAS/AD function under test. In this paper, a novel sensor modeling approach for automotive perception sensors is introduced. The novel approach combines kernel density estimation with regression modeling and puts the main focus on the position measurement errors. The modeling approach is designed for any automotive perception sensor that provides position estimations at the object level. To demonstrate and evaluate the new approach, a common state-of-the-art automotive camera (Mobileye 630) was considered. Both sensor measurements (Mobileye position estimations) and ground-truth data (DGPS positions of all attending vehicles) were collected during a large measurement campaign on a Hungarian highway to support the development and experimental validation of the new approach. The quality of the model was tested and compared to reference measurements, leading to a pointwise position error of 9.60% in the lateral and 1.57% in the longitudinal direction. Additionally, the modeling of the natural scattering of the sensor model output was satisfying. In particular, the deviations of the position measurements were well modeled with this approach.

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Section: Scope Of Workmentioning

confidence: 99%

Section: Campaign Descriptionmentioning

confidence: 99%

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Development and Experimental Validation of an Intelligent Camera Model for Automated Driving

Genser

Muckenhuber

Solmaz

et al. 2021

Sensors

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“…The construction of a close road section facilitates the development and testing of connected and autonomous vehicles. The total length of the test road section is 3.4 km [41]. In order to perfectly duplicate the real-world test scenario in the simulation environment, the M86 road was converted into an Ultra-High-Definition (UHD) map, a digital twin of reality that accurately represents every detail of the test environment.…”

Section: Ground Truth Definitionmentioning

confidence: 99%

“…In order to perfectly duplicate the real-world test scenario in the simulation environment, the M86 road was converted into an Ultra-High-Definition (UHD) map, a digital twin of reality that accurately represents every detail of the test environment. The production workflow that was applied for the production of the UHD map was presented in [41]. A digital twin-based M86 map was explicitly produced for testing and validating ADAS/AD driving functions with an absolute precision of +/−2 cm as a quality reference source.…”

Section: Ground Truth Definitionmentioning

confidence: 99%

Phenomenological Modelling of Camera Performance for Road Marking Detection

Tarik

Arefnezhad

et al. 2021

Energies

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With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles.

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Automatic Label Injection Into Local Infrastructure LiDAR Point Cloud for Training Data Set Generation

2022

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The representation of objects in LiDAR point clouds is changed as the height of the mounting position of sensor devices gets increased. Most of the available open datasets for training machine learning based object detectors are generated with vehicle top mounted sensors, thus the detectors trained on such datasets perform weaker when the sensor is observing the scene from a significantly higher viewpoint (e.g. infrastructure sensor). In this paper a novel Automatic Label Injection method is proposed to label the objects in the point cloud of the high-mounted infrastructure LiDAR sensor based on the output of a well performing "trainer" detector deployed at optimal height while considering the uncertainties caused by various factors described in detail throughout the paper. The proposed automatic labeling approach has been validated on a small scale sensor setup in a real-world traffic scenario where accurate differential GNSS reference data where also available for each test vehicle. Furthermore, the concept of a distributed multi-sensor system covering a larger area aimed for automatic dataset generation is also presented. It is shown that a machine learning based detector trained on differential GNSS-based training dataset performs very similarly to the detector retrained on a dataset generated by the proposed Automatic Label Injection technique. According to our results a significant increase in the maximum detection range can be achieved by retraining the detector on viewpoint specific data generated fully automatically by the proposed label injection technique compared to a detector trained on vehicle top mounted sensor data. INDEX TERMS object label,automatic label injection, roadside infrastructure sensors, training dataset I. INTRODUCTION

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Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Cited by 11 publications

References 4 publications

Development and Experimental Validation of an Intelligent Camera Model for Automated Driving

Development and Experimental Validation of an Intelligent Camera Model for Automated Driving

Phenomenological Modelling of Camera Performance for Road Marking Detection

Automatic Label Injection Into Local Infrastructure LiDAR Point Cloud for Training Data Set Generation

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