HYPERPROPERTIES-BASED OPTICAL FLOW-BASED AUTONOMOUS DRIVING SYSTEM

This study presents an autonomous driving system based on the principles of trace vectors derived from hyperproperty of a modified optical flowalgorithm. This technique allows keeping track of the past motion vectors by tracking the constraint sets to overcome the non-linear attributes ofthe deformable feature points and motion vectors. The results presented in this work exhibits stable tracking and multi-step prediction in a limitednumber of steps with less training vectors

Contextual information aided target tracking and path planning for autonomous ground vehicles

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management

Ground moving target tracking with space-time adaptive radar

Ground moving target tracking by airborne radar provides situational awareness of vehicle movements in the supervised region. Vehicles are detected by applying space time adaptive processing to the received multi channel radar data. The detections are then fed to a tracking algorithm that processes them to tracks. In literature, radar signal processing and ground target tracking are treated as two separate topics and results are not validated by experimental data. The first objective of this thesis is to provide a closer link between these fields. The second objective is to show that tracking performance can be improved by providing additional data from the radar signal processing to the tracking step. The third objective is to validate the algorithm and the performance improvement using experimental data. As a result this thesis presents a unified treatment of ground moving target tracking from radar raw data to established tracks. A complete reference algorithm for ground moving target tracking based on the Gaussian mixture probability hypothesis density filter is presented. In particular, Jacobians of the observation process are derived. They are presented in such a form that immediate implementation in a programming language is possible. In the course of this thesis a measurement campaign with the experimental radar PAMIR of Fraunhofer FHR was conducted. The experiment included two GPS equipped reference vehicles and a multitude of targets of opportunity. Tracking results obtained with this experimental data and the reference tracking algorithm of this thesis are shown. The thesis also enhances the reference target tracking algorithm by a parameter that characterizes the variance of the direction of arrival measurement of the target signal. This parameter is determined adaptively depending on the estimated signal strength and the clutter background. The major contribution with regard to this enhancement is a thorough experimental validation: Firstly, a comparison between GPS based measurements and radar based measurements of the direction of arrival shows that this variance captures the distribution of measurement errors excellently. Secondly, tracking results are compared to the GPS tracks of the ground truth vehicles. It is found that the enhanced algorithm yields superior track quality with respect to both track accuracy and track continuity.Bodenzielverfolgung mit luftgestütztem Radar liefert das Lagebild von Fahrzeug­bewegungen innerhalb des beobachteten Gebiets. Fahrzeuge werden durch die Anwendung von Raum-Zeit adaptiver Signalverarbeitung (STAP) entdeckt. Die Entdeckungen werden dann von einem Zielverfolgungsalgorithmus zu Zielspuren verarbeitet. In der Literatur werden Radarsignalverarbeitung und Zielverfolgung als zwei getrennte Forschungsfelder behandelt und die Bodenzielverfolgung wird nicht anhand von Realdaten validiert. Das erste Ziel dieser Arbeit ist, eine engere Verbindung zwischen beiden Feldern herzustellen. Das zweite Ziel ist zu zeigen, dass die Qualität der Zielverfolgung durch das Verwenden zusätzlicher, durch die Radarsignalverarbeitung gewonnene Information verbessert werden kann. Das dritte Ziel ist, die Funktionalität der Zielverfolgung und die Verbesserung der Leistung durch experimentelle Realdaten zu belegen. Somit stellt diese Arbeit eine Gesamtbehandlung der Bodenzielverfolgung von den Radar-Rohdaten bis zu Zielspuren dar. Es wird ein vollständiger, auf dem Gaussian Mixture Probability Hypothesis Density Filter basierender Referenzalgorithmus für die Bodenzielverfolgung entwickelt. Insbesondere werden Jacobimatrizen der Beobachtungsfunktion hergeleitet. Sie werden in der Arbeit so dargestellt, dass sie direkt in einer Programmiersprache implementiert werden können. Im Zuge dieser Arbeit wurde ein Zielverfolgungs-Experiment mit dem Experimentalsystem PAMIR des Fraunhofer FHR durchgeführt. In dem Experiment wurden neben einer Vielzahl von Gelegenheitszielen zwei mit GPS-Geräten ausgerüstete Fahrzeuge von dem Radar beobachtet. Auf Basis dieses Experiments und des Referenzalgorithmus werden Zielverfolgungsergebnisse vorgestellt. Darüber hinaus erweitert diese Arbeit den Referenzalgorithmus um einen Parameter, der die Varianz der Richtungsschätzung des Zielsignals charakterisiert. Dieser Parameter wird adaptiv anhand der geschätzten Signalstärke und der Stärke störender Bodenrückstreuungen festgelegt. Der wesentliche Beitrag dieser Arbeit in Bezug auf diese Erweiterung ist eine gründliche experimentelle Validierung. Erstens zeigt der Vergleich von GPS- und Radar-basierten Richtungsschätzungen, dass dieser Parameter die Verteilung des Messfehlers exzellent beschreibt. Zweitens werden Zielverfolgungsergebnisse mit den GPS-Spuren verglichen. Es zeigt sich, dass der erweiterte Algorithmus sowohl in Bezug auf die Spurgenauigkeit als auch in Bezug auf die Spurkontinuität die Zielverfolgung verbessert

Multiple Space Object Tracking Using A Randomized Hypothesis Generation Technique

In order to protect assets and operations in space, it is critical to collect and maintain accurate information regarding Resident Space Objects (RSOs). This collection of information is typically known as Space Situational Awareness (SSA). Ground-based and space-based sensors provide information regarding the RSOs in the form of observations or measurement returns. However, the distance between RSO and sensor can, at times, be tens of thousands of kilometers. This and other factors lead to noisy measurements that, in turn, cause one to be uncertain about which RSO a measurement belongs to. These ambiguities are known as data association ambiguities. Coupled with uncertainty in RSO state and the vast number of objects in space, data association ambiguities can cause the multiple space object-tracking problem to become computationally intractable. Tracking the RSO can be framed as a recursive Bayesian multiple object tracking problem with state space containing both continuous and discrete random variables. Using a Finite Set Statistics (FISST) approach one can derive the Random Finite Set (RFS) based Bayesian multiple object tracking recursions. These equations, known as the FISST multiple object tracking equations, are computationally intractable when solved in full. This computational intractability provokes the idea of the newly developed alternative hypothesis dependent derivation of the FISST equations. This alternative derivation allows for a Markov Chain Monte Carlo (MCMC) based randomized sampling technique, termed Randomized FISST (R-FISST). R-FISST is found to provide an accurate approximation of the full FISST recursions while keeping the problem tractable. There are many other benefits to this new derivation. For example, it can be used to connect and compare the classical tracking methods to the modern FISST based approaches. This connection clearly defines the relationships between different approaches and shows that they result in the same formulation for scenarios with a fixed number of objects and are very similar in cases with a varying number of objects. Findings also show that the R-FISST technique is compatible with many powerful optimization tools and can be scaled to solve problems such as collisional cascading

Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios

Multi-target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. Besides, the key problem of data association needs to be handled effectively considering the limitations in the computational resources on-board an autonomous car. The challenge of the tracking problem is further evident in the use of high-resolution automotive sensors which return multiple detections per object. Furthermore, it is customary to use multiple sensors that cover different and/or over-lapping Field of View and fuse sensor detections to provide robust and reliable tracking. As a consequence, in high-resolution multi-sensor settings, the data association uncertainty, and the corresponding tracking complexity increases pointing to a systematic approach to handle and process sensor detections. In this work, we present a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management features. These tracking functionalities can help facilitate perception during common events in public traffic as participants (suddenly) change lanes, navigate intersections, overtake and/or brake in emergencies, etc. Various tracking approaches including the ones based on joint integrated probability data association (JIPDA) filter, Linear Multi-target Integrated Probabilistic Data Association (LMIPDA) Filter, and their multi-detection variants are adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The utility of the filtering module is further elaborated by integrating it into a trajectory tracking problem based on model predictive control. To cope with tracking complexity in the case of multiple high-resolution sensors, we propose a hybrid scheme that combines the approaches of data clustering at the local sensor and multiple detections tracking schemes at the fusion layer. We implement a track-to-track fusion scheme that de-correlates local (sensor) tracks to avoid double counting and apply a measurement partitioning scheme to re-purpose the LMIPDA tracking algorithm to multi-detection cases. In addition to the measurement partitioning approach, a joint extent and kinematic state estimation scheme are integrated into the LMIPDA approach to facilitate perception and tracking of an individual as well as group targets as applied to multi-lane public traffic. We formulate the tracking problem as a two hierarchical layer. This arrangement enhances the multi-target tracking performance in situations including but not limited to target initialization(birth process), target occlusion, missed detections, unresolved measurement, target maneuver, etc. Also, target groups expose complex individual target interactions to help in situation assessment which is challenging to capture otherwise. The simulation studies are complemented by experimental studies performed on single and multiple (group) targets. Target detections are collected from a high-resolution radar at a frequency of 20Hz; whereas RTK-GPS data is made available as ground truth for one of the target vehicle\u27s trajectory

Bio-Radar: sistema de aquisição de sinais vitais sem contacto

The Bio-Radar system is capable to measure vital signs accurately, namely the respiratory and cardiac signal, using electromagnetic waves. In this way, it is possible to monitor subjects remotely and comfortably for long periods of time. This system is based on the micro-Doppler effect, which relates the received signal phase variation with the distance change between the subject chest-wall and the radar antennas, which occurs due to the cardiopulmonary function. Considering the variety of applications where this system can be used, it is required to evaluate its performance when applied to real context scenarios and thus demonstrate the advantages that bioradar systems can bring to the general population. In this work, a bio-radar prototype was developed in order to verify the viability to be integrated in specific applications, using robust and low profile solutions that equally guarantee the general system performance while addressing the market needs. Considering these two perspectives to be improved, different level solutions were developed. On the hardware side, textile antennas were developed to be embedded in a car seat upholstery, thus reaching a low profile solution and easy to include in the industrialization process. Real context scenarios imply long-term monitoring periods, where involuntary body motion can occur producing high amplitude signals that overshadow the vital signs. Non-controlled monitoring environments might also produce time varying parasitic reflections that have a direct impact in the signal. Additionally, the subject's physical stature and posture during the monitoring period can have a different impact in the signals quality. Therefore, signal processing algorithms were developed to be robust to low quality signals and non-static scenarios. On the other hand, the bio-radar potential can also be maximized if the acquired signals are used pertinently to help identify the subject's psychophysiological state enabling one to act accordingly. The random body motion until now has been seen as a noisy source, however it can also provide useful information regarding subject's state. In this sense, the acquired vital signs as well as other body motions were used in machine learning algorithms with the goal to identify the subject's emotions and thus verify if the remotely acquired vital signs can also provide useful information.O sistema Bio-Radar permite medir sinais vitais com precisão, nomeadamente o sinal respiratório e cardíaco, utilizando ondas eletromagnéticas para esse fim. Desta forma, é possível monitorizar sujeitos de forma remota e confortável durante longos períodos de tempo. Este sistema é baseado no efeito de micro-Doppler, que relaciona a variação de fase do sinal recebido com a alteração da distância entre as antenas do radar e a caixa torácica do sujeito, que ocorre durante a função cardiopulmonar. Considerando a variedade de aplicações onde este sistema pode ser utilizado, é necessário avaliar o seu desempenho quando aplicado em contextos reais e assim demonstrar as vantagens que os sistemas bio-radar podem trazer à população geral. Neste trabalho, foi desenvolvido um protótipo do bio radar com o objetivo de verificar a viabilidade de integrar estes sistemas em aplicações específicas, utilizando soluções robustas e discretas que garantam igualmente o seu bom desempenho, indo simultaneamente de encontro às necessidades do mercado. Considerando estas duas perspetivas em que o sistema pode ser melhorado, foram desenvolvidas soluções de diferentes níveis. Do ponto de vista de hardware, foram desenvolvidas antenas têxteis para serem integradas no estofo de um banco automóvel, alcançando uma solução discreta e fácil de incluir num processo de industrialização. Contextos reais de aplicação implicam períodos de monitorização longos, onde podem ocorrer movimentos corporais involuntários que produzem sinais de elevada amplitude que se sobrepõem aos sinais vitais. Ambientes de monitorização não controlados podem produzir reflexões parasitas variantes no tempo que têm impacto direto no sinal. Adicionalmente, a estrutura física do sujeito e a sua postura durante o período de monitorização podem ter impactos diferentes na qualidade dos sinais. Desta forma, foram desenvolvidos algoritmos de processamento de sinal robustos a sinais de baixa qualidade e a cenários não estáticos. Por outro lado, o potencial do bio radar pode também ser maximizado se os sinais adquiridos forem pertinentemente utilizados de forma a ajudar a identificar o estado psicofisiológico do sujeito, permitindo mais tarde agir em conformidade. O movimento corporal aleatório que foi até agora visto como uma fonte de ruído, pode no entanto também fornecer informação útil sobre o estado do sujeito. Neste sentido, os sinais vitais e outros movimentos corporais adquiridos foram utilizados em algoritmos de aprendizagem automática com o objetivo de identificar as emoções do sujeito e assim verificar que sinais vitais adquiridos remotamente podem também conter informação útil.Programa Doutoral em Engenharia Eletrotécnic

Multi-Robot Active Information Gathering Using Random Finite Sets

Many tasks in the modern world involve collecting information, such as infrastructure inspection, security and surveillance, environmental monitoring, and search and rescue. All of these tasks involve searching an environment to detect, localize, and track objects of interest, such as damage to roadways, suspicious packages, plant species, or victims of a natural disaster. In any of these tasks the number of objects of interest is often not known at the onset of exploration. Teams of robots can automate these often dull, dirty, or dangerous tasks to decrease costs and improve speed and safety. This dissertation addresses the problem of automating data collection processes, so that a team of mobile sensor platforms is able to explore an environment to determine the number of objects of interest and their locations. In real-world scenarios, robots may fail to detect objects within the field of view, receive false positive measurements to clutter objects, and be unable to disambiguate true objects. This makes data association, i.e., matching individual measurements to targets, difficult. To account for this, we utilize filtering algorithms based on random finite sets to simultaneously estimate the number of objects and their locations within the environment without the need to explicitly consider data association. Using the resulting estimates they receive, robots choose actions that maximize the mutual information between the set of targets and the binary events of receiving no detections. This effectively hedges against uninformative actions and leads to a closed form equation to compute mutual information, allowing the robot team to plan over a long time horizon. The robots either communicate with a central agent, which performs the estimation and control computations, or act in a decentralized manner. Our extensive hardware and simulated experiments validate the unified estimation and control framework, using robots with a wide variety of mobility and sensing capabilities to showcase the broad applicability of the framework