scholarly journals Pedestrian recognition using automotive radar sensors

2012 ◽  
Vol 10 ◽  
pp. 45-55 ◽  
Author(s):  
A. Bartsch ◽  
F. Fitzek ◽  
R. H. Rasshofer
Keyword(s):  
Machine Learning ◽  
Elevation Angle ◽  
Real Life ◽  
Doppler Frequency ◽  
Radar Data ◽  
Limiting Factors ◽  
Automotive Radar ◽  
Signal Processing Algorithm ◽  
Radar Sensors ◽  
The Impact

Abstract. The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.

scholarly journals Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3410
Author(s):  
Claudia Malzer ◽  
Marcus Baum
Keyword(s):  
Moving Objects ◽  
Hierarchical Cluster ◽  
Autonomous Driving ◽  
Radar Data ◽  
Background Information ◽  
Automotive Radar ◽  
Data Set ◽  
Radar Sensors ◽  
Small Clusters ◽  
Cluster Level

High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments.

Development of Warning System and Driving Model for Overtaking Trucks on Two-Lane Highways

2019 ◽  
Vol 2673 (11) ◽  
pp. 1-12 ◽  
Author(s):  
Udai Hassein ◽  
Maksym Diachuk ◽  
Said Easa
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Driving Simulator ◽  
Real Life ◽  
Warning System ◽  
Steering Control ◽  
Collision Warning ◽  
Radar Sensors ◽  
Sight Distance ◽  
The Impact

Passing collisions are one of the most serious traffic safety problems on two-lane highways. These collisions occur when a driver overestimates the available sight distance. This paper presents a framework for a passing collision warning system (PCWS) that assists drivers in avoiding passing collisions by reducing the likelihood of human error. The system uses a combination of a camera and radar sensors to identify the impeding vehicle type and to detect the opposing vehicles traveling in the left lane. The study involved the development of a steering control model providing lane-change maneuvers, the design of a driving simulator experiment that allows for the collection of data necessary to estimate passing parameters, and the elaboration of the algorithm for the PCWS based on sensor signals to detect impeding vehicles such as trucks. Simulation tests were carried out to confirm the effectiveness of the proposed PCWS algorithm. The impact of driver behavior on passing maneuvers was also investigated. Mathematical and imitation models were enhanced to implement Simulink for replications of real-life driving scenarios. The different factors that affect system accuracy were also examined.

scholarly journals Method for increasing noise resistance of radar sensors with frequency selection

2021 ◽  
Vol 5 (3) ◽  
pp. 284-304
Author(s):  
V. Ya. Noskov ◽  
◽  
E. V. Bogatyrev ◽  
K. A. Ignatkov ◽  
K. D. Shaidurov ◽  
...  
Keyword(s):  
Frequency Control ◽  
Experimental Studies ◽  
Doppler Frequency ◽  
Third Party ◽  
Signal Spectrum ◽  
Automotive Radar ◽  
Radar Sensors ◽  
Average Value ◽  
Direction Of Movement ◽  
The Stability

The description of a new method of signal generation and processing which provides an increase in the noise immunity of radar sensors (RS) with frequency switching (FS) radiation is presented. The principle of method is in the use of a set of time intervals when measuring the phase difference of signals at different radiation frequencies and, accordingly, a set of the Doppler frequency values in the signal spectrum when determining the average value of the Doppler frequency, as well as the use of forward and reverse IF sequences. This method allows averaging the results of calculating individual implementations and, thereby, increase the accuracy of determining the target speed and distance to it. At the same time, the stability of the RS with FS also increases to the effects of signals from third-party radio sources and interference from the underlying surface. The results of experimental studies of the method are obtained on the example of the autodyne RS with the 8-mm frequency range, made on the basis of the Gann diode generator with frequency control by varicap. The method may be used in on-board (for example, automotive) radar sensors designed to detect moving targets, measure the distance to them, as well as determine the speed and direction of movement.

Learning-based error modeling in FDM 3D printing process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paschalis Charalampous ◽  
Ioannis Kostavelis ◽  
Theodora Kontodina ◽  
Dimitrios Tzovaras
Keyword(s):  
Machine Learning ◽  
Computer Aided Design ◽  
Fused Deposition Modeling ◽  
Real Life ◽  
Deep Understanding ◽  
Error Modeling ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Fused Deposition ◽  
The Impact

Purpose Additive manufacturing (AM) technologies are gaining immense popularity in the manufacturing sector because of their undisputed ability to construct geometrically complex prototypes and functional parts. However, the reliability of AM processes in providing high-quality products remains an open and challenging task, as it necessitates a deep understanding of the impact of process-related parameters on certain characteristics of the manufactured part. The purpose of this study is to develop a novel method for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure. Design/methodology/approach The introduced methodology uses regression-based machine learning algorithms to predict the dimensional deviations between the nominal computer aided design (CAD) model and the produced physical part. To achieve this, a database with measurements of three-dimensional (3D) printed parts possessing primitive geometry was created for the formulation of the predictive models. Additionally, adjustments on the dimensions of the 3D model are also considered to compensate for the overall shape deviations and further improve the accuracy of the process. Findings The validity of the suggested strategy is evaluated in a real-life manufacturing scenario with a complex benchmark model and a freeform shape manufactured in different scaling factors, where various sets of printing conditions have been applied. The experimental results exhibited that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors as well. Originality/value The present research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process.

Angular measurements in azimuth and elevation with 77 GHz radar sensors

2013 ◽  
Vol 5 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Klaus Baur ◽  
Marcel Mayer ◽  
Steffen Lutz ◽  
Thomas Walter
Keyword(s):  
Elevation Angle ◽  
Azimuth Angle ◽  
Radar Signal ◽  
Automotive Radar ◽  
Radar Sensor ◽  
Radar Sensors ◽  
Dielectric Lens ◽  
Angular Measurements ◽  
Array Structure ◽  
77 Ghz

An antenna concept for direction of arrival estimation in azimuth and elevation is proposed for 77 GHz automotive radar sensors. This concept uses the amplitude information of the radar signal for the azimuth angle and the phase information for the elevation angle. The antenna consists of a combination of a series-fed-array structure with a cylindrical dielectric lens. This concept is implemented into a radar sensor based on SiGe MMICs for validation. A two- and a four-beam configuration are presented and discussed with respect to angular accuracy and ambiguities.

scholarly journals Automotive Radar in a UAV to Assess Earth Surface Processes and Land Responses

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4463
Author(s):  
Christoph Weber ◽  
Johannes von Eichel-Streiber ◽  
Jesús Rodrigo-Comino ◽  
Jens Altenburg ◽  
Thomas Udelhoven
Keyword(s):  
Earth Science ◽  
Low Cost ◽  
Science Research ◽  
Radar Data ◽  
Current Status ◽  
Automotive Radar ◽  
Vast Number ◽  
Radar Sensors ◽  
Before And After ◽  
Insight Into

The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to assess the current status of specific landforms such as gullies, rills, or ravines. The UAV equipped sensors are a key part to success. Besides commonly used sensors such as cameras, radar sensors are another possibility. They are less known for this application, but already well established in research. A vast number of research projects use professional radars, but they are expensive and difficult to handle. Therefore, the use of low-cost radar sensors is becoming more relevant. In this article, to make the usage of radar simpler and more efficient, we developed with automotive radar technology. We introduce basic radar techniques and present two radar sensors with their specifications. To record the radar data, we developed a system with an integrated camera and sensors. The weight of the whole system is about 315 g for the small radar and 450 g for the large one. The whole system was integrated into a UAV and test flights were performed. After that, several flights were carried out, to verify the system with both radar sensors. Thereby, the records provide an insight into the radar data. We demonstrated that the recording system works and the radar sensors are suitable for the usage in a UAV and future earth science research because of its autonomy, precision, and lightweight.

scholarly journals Dealing with Randomness and Concept Drift in Large Datasets

Data ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 77
Author(s):  
Kassim S. Mwitondi ◽  
Raed A. Said
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Data Science ◽  
Concept Drift ◽  
Real Life ◽  
Quality Education ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Address Data ◽  
The Impact

Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data -driven solutions hinges on two fronts-technical and application. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educationists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify.

scholarly journals Relationship between impact characteristics and launch direction in softball hitting: A study involving elite players

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260520
Author(s):  
Shuji Kidokoro ◽  
Yoshitaka Morishita
Keyword(s):  
Motion Capture ◽  
Elevation Angle ◽  
Limiting Factors ◽  
Stepwise Multiple Regression ◽  
Ball Impact ◽  
Exit Velocity ◽  
Opposite Field ◽  
The Common ◽  
Impact Characteristics ◽  
The Impact

In the game of softball, the batter should possess the necessary skills to hit the ball toward various directions with high initial speed. However, because various factors influence each other, there are limitations to the range that can be controlled by the batter’s skill. This study was aimed at extracting the impact characteristics associated with the launch speed/direction and batted ball spin and clarifying the important skills required to improve the batter’s hitting performance. In our experiments, 20 female softball players, who are members of the Japan women’s national softball team, hit balls launched from a pitching machine. The movements of the ball and bat before, during, or after the impact were recorded using a motion capture system. Stepwise multiple regression analysis was performed to extract factors relating the side spin rate. The undercut angle (elevation angle between the bat’s trajectory and the common normal between the ball and bat: ΔR2 = 0.560) and the horizontal bat angle (azimuth of bat’s long axis at ball impact: ΔR2 = 0.299) were strongly associated with the side spin rate (total R2 = 0.893, p < 0.001). The undercut angle in opposite-field hitting was significantly larger than that in pull-side hitting (p < 0.001). The side spin rate was associated with the undercut angle because the bat’s distal (barrel) side inclined downward (–29.6 ± 8.7°) at impact. The ball exit velocity was higher when it was hit at a smaller undercut angle (R2 = 0.523, p < 0.001). Therefore, it is deemed desirable to focus on maximizing the ball exit velocity rather than ball spin because the ball–bat impact characteristics vary inevitably depending on the launch direction. Meanwhile, the use of the ball delivery machine and the slower pitched ball are the limiting factors in the generalization of the findings.

scholarly journals Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

2021 ◽  
Vol 2022 (1) ◽  
pp. 274-290
Author(s):  
Dmitrii Usynin ◽  
Daniel Rueckert ◽  
Jonathan Passerat-Palmbach ◽  
Georgios Kaissis
Keyword(s):  
Machine Learning ◽  
Collaborative Learning ◽  
Real Life ◽  
Model Adaptation ◽  
Theoretical Understanding ◽  
Model Inversion ◽  
Optimal Balance ◽  
Learning Scenarios ◽  
Utility Cost ◽  
The Impact

Abstract In this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We refer to such mitigations as model adaptation. Through extensive experimentation on both, benchmark and real-life datasets, we establish a more practical threat model for collaborative learning scenarios. In particular, we evaluate the impact of model adaptation by implementing a range of attacks belonging to the broader categories of model inversion and membership inference. Our experiments yield two noteworthy outcomes: they demonstrate the difficulty of actually conducting successful attacks under realistic settings when model adaptation is employed and they highlight the challenge inherent in successfully combining model adaptation and formal privacy-preserving techniques to retain the optimal balance between model utility and attack resilience.

scholarly journals DETECTION AND CLASSIFICATION OF BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA AND MACHINE LEARNING APPROACHES

2020 ◽  
Vol V-1-2020 ◽  
pp. 109-116
Author(s):  
M. Arnold ◽  
S. Keller
Keyword(s):  
Machine Learning ◽  
Methodological Approach ◽  
Ground Truth ◽  
Vertical Displacement ◽  
Radar Data ◽  
Traffic Load ◽  
Learning Approaches ◽  
Adaptive Boosting ◽  
Low Pass ◽  
The Impact

Abstract. In this paper, we investigate the potential of detecting and classifying vehicle crossings (events) on bridges with ground-based interferometric radar (GBR) data and machine learning (ML) approaches. The GBR data and image data recorded by a unmanned aerial vehicle, used as ground truth, have been measured during field campaigns at three bridges in Germany non-invasively. Since traffic load of the bridges has taken place during the measurement, we have been able to monitor the bridge dynamics in terms of a vertical displacement. We introduce a methodological approach with three steps including preprocessing of the GBR data, feature extraction and well-chosen ML models. The impact of the preprocessing approaches as well as of the selected features on the classification results is evaluated. In case of the distinction between event and no event, adaptive boosting with low-pass filtering achieves the best classification results. Regarding the distinction between different class types of vehicles, random forest performs best utilising low-pass filtered GBR data. Our results reveal the potential of the GBR data combined with the respective methodological approach to detect and to classify events under real-world conditions. In conclusion, the preliminary results of this paper provide a basis for further improvements such as advanced preprocessing of the GBR data to extracted additional features which then can be used as input for the ML models.

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