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Radar-Detection Based Classification of Moving Objects Using Machine Learning Methods

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Radar-Detection Based Classification of Moving Objects Using Machine Learning Methods Radar-detection based classification of moving objects using machine learning methods VICTOR NORDENMARK ADAM FORSGREN Master of Science Thesis Stockholm, Sweden 2015 Radar-detection based classification of moving objects using machine learning methods Victor Nordenmark Adam Forsgren Master of Science Thesis MMK 2015:77 MDA 520 KTH Industrial Engineering and Management Machine Design SE-100 44 STOCKHOLM ii Examensarbete MMK 2015:77 MDA 520 Radar-detection based classification of moving objects using machine learning methods Victor Nordenmark Adam Forsgren Godkänt Examinator Handledare 2015-06-17 Martin Grimheden De-Jiu Chen Uppdragsgivare Kontaktperson Scania Södertälje AB Kristian Lundh Sammanfattning I detta examensarbete undersöks möjligheten att klassificera rörliga objekt baserat på data från Dopplerradardetektioner. Slutmålet är ett system som använder billig hårdvara och utför beräkningar av låg komplexitet. Scania, företaget som har beställt detta projekt, är intresserat av användningspotentialen för ett sådant system i applikationer för autonoma fordon. Specifikt vill Scania använda klassinformationen för att lättare kunna följa rörliga objekt, vilket är en väsentlig färdighet för en autonomt körande lastbil. Objekten delas in i fyra klasser: fotgängare, cyklist, bil och lastbil. Indatan till systemet består väsentligen av en plattform med fyra stycken monopulsdopplerradars som arbetar med en vågfrekvens på 77 GHz. Ett klassificeringssystem baserat på maskininlärningskonceptet Support vector machines har skapats. Detta system har tränats och validerats på ett dataset som insamlats för projektet, innehållandes datapunkter med klassetiketter. Ett antal stödfunktioner till detta system har också skapats och testats. Klassificeraren visas kunna skilja väl på de fyra klasserna i valideringssteget. Simuleringar av det kompletta systemet gjort på inspelade loggar med radardata visar lovande resultat, även i situationer som inte finns representerade i träningsdatan. För att vidare undersöka systemet har det implementerats och testats på prototyplastbilen Astator, och prestandan har utvärderats utifrån både realtidstidsperpsektiv och klassificeringsnoggranhet. Generellt uppvisar systemet lovande resultat i scenarier som liknar slutanvändningsområdet. I mer komplexa trafiksituationer och då lastbilen färdas i högre hastigheter leder dock en högre förekomst av sensorbrus till att systemets prestanda försämras. iii Master of Science Thesis 2015:77 MDA 520 Klassificering av rörliga objekt baserat på radardetektioner med hjälp av maskininlärningsmetoder Victor Nordenmark Adam Forsgren Approved Examiner Supervisor 2015-06-17 Martin Grimheden De-Jiu Chen Commissioner Contact person Scania Södertälje AB Kristian Lundh Abstract In this MSc thesis, the possibility to classify moving objects based on radar detection data is investigated. The intention is a light-weight, low-level system that relies on cheap hardware and calculations of low complexity. Scania, the company that has commissioned this project, is interested in the usage potential of such a system in autonomous vehicle applications. Specifically, the class information is desired in order to enhance the moving object tracker, a subsystem that represents a crucial skillset of an autonomously driving truck. Objects are classified as belonging to one of four classes: Pedestrian, bicyclist, personal vehicle and truck. The major system input consists of sensor data from a set of four short-range mono-pulse Doppler radars operating at 77 GHz. Using a set of training and validation data gathered and labeled within this project, a classification system based on the machine learning method of Support vector machines is created. Several other supporting software structures are also created and evaluated. In the validation phase, the system is shown to discern well between the four classes. System simulations performed on logged radar data show promising performance also in situations not reflected within the labeled dataset. To further investigate the feasibility of the system, it has been implemented and tested on the prototype test vehicle Astator, and performance has been evaluated with regards to both real-time constraints and classification accuracy. Overall, the system shows promise in the scenarios for which it was intended, both with respect to real-time and classification performance. In more complex scenarios however, sensor noise is increasingly apparent and affects the system performance in a negative way. The noise is extra apparent in heavy traffic and high velocity scenarios. iv List of Figures 1 Schematic overview of relevant target system architecture......4 2 Approximate placement and FOV of SRR sensors on Astator....5 3 Development approach schematic................... 10 4 Coordinate systems........................... 18 5 Projection of radar EGO component on the range rate of a detection. 19 6 Linear decision boundaries in two and three dimensions....... 21 7 An example of a non-linearly separable dataset............ 21 8 Illustration of the bias-variance dilemma............... 24 9 The support vector machine visualized in a two-dimensional feature space................................... 26 10 Illustration of the kernel concept................... 29 11 Illustration of soft-margin SVM.................... 31 12 DBSCAN clustering method...................... 37 13 Development process and classification system overview....... 44 14 The eps tradeoff............................. 54 15 Timing Diagram............................ 64 16 Biplot of features and training data projected onto first two PC.. 70 17 Training data and biplot projected onto first three PC....... 71 18 Lines of noise detections behind an object.............. 74 19 Scattered noise detections behind an object............. 75 20 Clusters of noise detections with high velocity values........ 76 21 Fence detections and noise when moving at 60 km/h........ 77 22 Radar Detections Clustered with Eps = 4 meters.......... 82 23 Coarse grid search for SVM parameters................ 84 24 Fine grid search for SVM parameters................. 84 25 Typical frames from evaluation logs.................. 91 26 Typical frame from highway log.................... 91 v List of Tables 1 Delphi SRR Midrange specifications..................5 2 Binary classification confusion matrix................. 34 3 Multiclass Confusion matrix...................... 62 4 Classification performance measurements............... 62 5 Radar detection clusters gathered and labeled............ 67 6 Mean and variance of features used for object description...... 69 7 Table of the cumulative variance explanation per principal component 70 8 maxdR detection filter statistics.................... 79 9 maxClusterVelVar filtering results................... 79 10 minClusterAmpVar filtering results.................. 80 11 Offline evaluation of Classification performance........... 85 12 Real-time simlation performance from Simulink Profiler....... 87 13 Real-time performance on the target system............. 88 14 Input output comparison of the two systems for the different classes 89 15 Classification system evaluation on log with trailer.......... 90 16 Fulfillment of functional requirements................. 93 17 Fulfillment of extra-functional requirements............. 94 vi Contents 1 Introduction 1.1 Project background................................1 1.1.1 General project background...............................1 1.1.2 Problem description....................................2 1.1.3 Difference to other projects...............................2 1.2 Project goal......................................3 1.2.1 Project purpose.......................................3 1.2.2 Target system.........................................3 1.2.3 Project requirements....................................7 1.2.4 Research questions.....................................9 1.3 Project development methodology and considerations........9 1.3.1 Development approach..................................9 1.3.2 Delimitations......................................... 11 1.3.3 Sustainable development considerations...................... 12 2 Frame of reference 2.1 Previous work.................................... 13 2.1.1 Radar based vehicle perception............................ 13 2.1.2 Doppler radar as input to learning systems.................... 15 2.2 Doppler radar perception and integration of multiple sensors.. 16 2.2.1 Basic Doppler radar theory............................... 16 2.2.2 Sensor fusion and integration.............................. 17 2.3 Theoretical overview of machine learning concepts and methods 19 2.3.1 Supervised learning, classification and overfitting............... 20 2.3.2 Support vector machines as a method for classification........... 25 2.3.3 Classification performance analysis.......................... 33 2.4 Extraction and analysis of object descriptions............. 36 2.4.1 Clustering of sensor data................................. 36 2.4.2 Selecting and extracting features from data clusters............. 39 2.4.3 Principal component analysis for feature evaluation............. 40 2.5 Frame of reference conclusions......................... 41 2.5.1 Current best-practice in vehicle perception.................... 42 2.5.2 Theory and methods employed............................ 43 3 Methods 3.1 Method overview and system introduction................ 44 3.1.1 Stages of system development............................. 44 3.1.2 Classification system overview............................
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