Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Automated and intelligent hacking detection system

Dissertação de mestrado integrado em Informatics EngineeringThe Controller Area Network (CAN) is the backbone of automotive networking, connecting many Electronic ControlUnits (ECUs) that control virtually every vehicle function from fuel injection to parking sensors. It possesses,however, no security functionality such as message encryption or authentication by default. Attackers can easily inject or modify packets in the network, causing vehicle malfunction and endangering the driver and passengers. There is an increasing number of ECUs in modern vehicles, primarily driven by the consumer’s expectation of more features and comfort in their vehicles as well as ever-stricter government regulations on efficiency and emissions. Combined with vehicle connectivity to the exterior via Bluetooth, Wi-Fi, or cellular, this raises the risk of attacks. Traditional networks, such as Internet Protocol (IP), typically have an Intrusion Detection System (IDS) analysing traffic and signalling when an attack occurs. The system here proposed is an adaptation of the traditional IDS into the CAN bus using a One Class Support Vector Machine (OCSVM) trained with live, attack-free traffic. The system is capable of reliably detecting a variety of attacks, both known and unknown, without needing to understand payload syntax, which is largely proprietary and vehicle/model dependent. This allows it to be installed in any vehicle in a plug-and-play fashion while maintaining a large degree of accuracy with very few false positives.A Controller Area Network (CAN) é a principal tecnologia de comunicação interna automóvel, ligando muitas Electronic Control Units (ECUs) que controlam virtualmente todas as funções do veículo desde injeção de combustível até aos sensores de estacionamento. No entanto, não possui por defeito funcionalidades de segurança como cifragem ou autenticação. É possível aos atacantes facilmente injetarem ou modificarem pacotes na rede causando estragos e colocando em perigo tanto o condutor como os passageiros. Existe um número cada vez maior de ECUs nos veículos modernos, impulsionado principalmente pelas expectativas do consumidores quanto ao aumento do conforto nos seus veículos, e pelos cada vez mais exigentes regulamentos de eficiência e emissões. Isto, associada à conexão ao exterior através de tecnologias como o Bluetooth, Wi-Fi, ou redes móveis, aumenta o risco de ataques. Redes tradicionais, como a rede Internet Protocol (IP), tipicamente possuem um Intrusion Detection Systems (IDSs) que analiza o tráfego e assinala a presença de um ataque. O sistema aqui proposto é uma adaptação do IDS tradicional à rede CAN utilizando uma One Class Support Vector Machine (OCSVM) treinada com tráfego real e livre de ataques. O sistema é capaz de detetar com fiabilidade uma variedade de ataques, tanto conhecidos como desconhecidos, sem a necessidade de entender a sintaxe do campo de dados das mensagens, que é maioritariamente proprietária. Isto permite ao sistema ser instalado em qualquer veículo num modo plug-and-play enquanto mantém um elevado nível de desempenho com muito poucos falsos positivos

Techniques for utilizing classification towards securing automotive controller area network and machine learning towards the reverse engineering of CAN messages

The vehicle industry is quickly becoming more connected and growing. This growth is due to advancements in cyber physical systems (CPSs) that enhance the safety and automation in vehicle. The modern automobile consists of more than 70 electronic control units (ECUs) that communicate and interact with each other over automotive bus systems. Passenger comforts, infotainment features, and connectivity continue to progress through the growth and integration of Internet-of-Things (IoT) technologies. Common networks include the Controller Area Network (CAN), Local Interconnect Network (LIN), and FlexRay. However, the benefits of increased connectivity and features comes with the penalty of increased vulnerabilities. Security is lacking in preventing attacks on safety-critical control systems. I will explore the state of the art methods and approaches researchers have taken to identify threats and how to address them with intrusion detection. I discuss the development of a hybrid based intrusion detection approach that combines anomaly and signature based detection methods. Machine learning is a hot topic in security as it is a method of learning and classifying system behavior and can detect intrusions that alter normal behavior. In this paper, we discuss utilizing machine learning algorithms to assist in classifying CAN messages. I present work that focuses on the reverse engineering and classification of CAN messages. The problem is that even though CAN is standardized, the implementation may vary for different manufacturers and vehicle models. These implementations are kept secret, therefore CAN messages for every vehicle needs to be analyzed and reverse engineered in order to get information. Due to the lack of publicly available CAN specifications, attackers and researchers need to reverse engineer messages to pinpoint which messages will have the desired impact. The reverse engineering process is needed by researchers and hackers for all manufacturers and their respective vehicles to understand what the vehicle is doing and what each CAN message means. The knowledge of the specifications of CAN messages can improve the effectiveness of security mechanisms applied to CAN

Redundancy reduction in motor control

Research in machine learning and neuroscience has made remarkable progress by investigating statistical redundancy in representations of natural environments, but to date much of this work has focused on sensory information like images and sounds. This dissertation explores the notions of redundancy and efficiency in the motor domain, where several different forms of independence exist. The dissertation begins by discussing redundancy at a conceptual level and presents relevant background material. Next, three main branches of original research are described. The first branch consists of a novel control framework for integrating low-bandwidth sensory updates with model uncertainty and action selection for navigating complex, multi-task environments. The second branch of research applies existing machine learning techniques to movement information and explores the mismatch between these methods for extracting independent components and the forms of redundancy that exist in the motor domain. The third branch of work analyzes full-body, goal-directed reaching movements gathered in a novel laboratory experiment, using explicitly measured information about the goal of each movement to uncover patterns in the movement dynamics. Each branch of research explores redundancy reduction in movement from a different perspective, building up a sort of catalog of the types of information present in movements. Redundancy is discussed throughout as an an important aspect of movement in the natural world. The dissertation concludes by summarizing the contributions of these three branches of work, and discussing promising areas for future work spurred by these investigations. More detailed models of voluntary movements hold promise not only for better treatments, improved prosthetics, smoother animations, and more fluid robots, but also as an avenue for scientific insight into the very foundations of cognition.Computer Science

Explainable shared control in assistive robotics

Shared control plays a pivotal role in designing assistive robots to complement human capabilities during everyday tasks. However, traditional shared control relies on users forming an accurate mental model of expected robot behaviour. Without this accurate mental image, users may encounter confusion or frustration whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. The Explainable Shared Control paradigm introduced in this thesis attempts to resolve such model misalignment by jointly considering assistance and transparency. There are two perspectives of transparency to Explainable Shared Control: the human's and the robot's. Augmented reality is presented as an integral component that addresses the human viewpoint by visually unveiling the robot's internal mechanisms. Whilst the robot perspective requires an awareness of human "intent", and so a clustering framework composed of a deep generative model is developed for human intention inference. Both transparency constructs are implemented atop a real assistive robotic wheelchair and tested with human users. An augmented reality headset is incorporated into the robotic wheelchair and different interface options are evaluated across two user studies to explore their influence on mental model accuracy. Experimental results indicate that this setup facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment. As for human intention inference, the clustering framework is applied to a dataset collected from users operating the robotic wheelchair. Findings from this experiment demonstrate that the learnt clusters are interpretable and meaningful representations of human intent. This thesis serves as a first step in the interdisciplinary area of Explainable Shared Control. The contributions to shared control, augmented reality and representation learning contained within this thesis are likely to help future research advance the proposed paradigm, and thus bolster the prevalence of assistive robots.Open Acces

AI-based intrusion detection systems for in-vehicle networks: a survey.

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyber attacks. Various Intrusion Detection Systems (IDSs) have been developed to detect these attacks. However, the high generalization capabilities of Artificial Intelligence (AI) make AI-based IDS an excellent countermeasure against automotive cyber attacks. This article surveys AI-based in-vehicle IDS from 2016 to 2022 (August) with a novel taxonomy. It reviews the detection techniques, attack types, features, and benchmark datasets. Furthermore, the article discusses the security of AI models, necessary steps to develop AI-based IDSs in the CAN bus, identifies the limitations of existing proposals, and gives recommendations for future research directions

Advanced driver assistance system based on computer vision using detection, recognition and tracking of road signs

Los accidentes de tráfico son un grave problema socioeconómico. Obviamente el coste humano es imposible de evaluar y el económico supone un continuo e ingente gasto de dinero por parte de los gobiernos. Se han propuesto diferentes soluciones para paliar los efectos de los accidentes, una de las cuales, los Sistemas Avanzados de Ayuda a la Conducción, son el marco en el que se encuadra el presente trabajo. Estos sistemas, como su nombre indica, asisten al conductor ofreciéndole información del entorno o actuando en determinadas circunstancias para la salvaguarda de los ocupantes del vehículo, o para facilitar la conducción. El sistema que se propone en esta tesis es una plataforma multipropósito original en su concepción, cuyo fin más inmediato es reconocer las señales de tráfico de prohibición, peligro, ceda el paso, obligación e indicación. La información obtenida de ese reconocimiento se integra dentro de un módulo de aviso al conductor. Lo que se pretende es que el conductor conozca en todo momento si está contraviniendo alguna norma de circulación derivada de una velocidad o maniobra inadecuada para el tipo de señal que se ha reconocido. Dado que este sistema está embarcado en un vehículo, deberá cumplir dos requisitos de especial importancia: funcionar en tiempo real y tanto en entorno urbano como en autopista. El primero cobra especial relevancia si se piensa en que la seguridad de los ocupantes del vehículo y de los peatones puede depender de los avisos que permitan al conductor anticiparse a un peligro. La segunda, que es una aportación original, garantiza que el sistema funcionará en vías donde la velocidad es mayor y por tanto también la probabilidad y gravedad de un posible accidente. El sistema, como se decía anteriormente, sirve al desarrollo de otras aplicaciones, como es el caso del inventariado automático de señales de tráfico, tan en auge actualmente. ______________________________________________Road traffic accidents are a serious socio-economic problem, where the cost of human life is impossible to evaluate, and cause massive and continuous government spending. Different solutions have been proposed to reduce the effects of accidents, one of which, Advanced Driver Assistance Systems, forms part of the framework which encompasses the current investigation work presented in this thesis. These systems, as their name suggest, assist the driver by providing vital information on the traffic environment or by acting under speciffic circumstances to safeguard the occupants of the vehicle, or to facilitate driving. A multitask driver assistance platform is originally presented as part of the research work of this thesis, among other tasks, it has been designed to recognize road signs in both urban and non-urban environments. The road signs that have been considered are: prohibition, danger, yield, obligation and indication. The information obtained from the road sign recognition process forms part of a complete module that advises drivers on traffic circulation requirements. The primary objective of this work has been to increase driver awareness, at all times, on the legal limitations which have established for road safety. The two areas which have been considered in this thesis are the velocity of the vehicle and the velocity of the vehicle corresponding to incorrect driving manoeuvres both of which are controlled using the information contained within road signs. The assistance platform which has been designed forms an integral part of the vehicle, thus it must satisfy two important requirements: first, provide the driver with real time information from road signs and secondly, to operate in both urban and non-urban environments. Real time information is important for the safety of drivers, passengers, and pedestrians where the information provided warns the driver well in advance of any danger so that the appropriate manoeuvres can be made to correct the speed of the vehicle. One important aspect of the work presented here is that the system has also been designed for non-urban environments, such as: national roads, toll roads and motorways where there is a higher probability of more serious and fatal accidents occurring due to the increased speed. There is a wide range of possible applications for road sign recognition systems, another area of interest which has motivated the work carried out for this thesis has been an automatic road sign inventory system. From the beginning of research in automatic road sign detection applications, many di®erent stages have been proposed, such as: signal detection, road sign recognition, and road sign tracking. The work presented in this thesis provides an in depth analysis of each of these three stages and this has allowed a more robust and complete system to be designed. In this thesis an exhaustive review is presented on color spaces and their characteristic color components which are best suited to the task of searching for road signs within an image. The technique of template matching using patterns with road signs has been optimized in this research, also included is an analysis of the most adequate models required to effciently detect each type of road sign. As part of the development of the recognition stage of the system, the two currently most important tools used in object recognition have been studied, these are: template matching and neural networks (NN). A comparative analysis of both of these techniques has been performed, where emphasis has been placed on image preprocessing to optimize the results. The final stage of this system addresses the problem of road sign tracking. The method proposed is a model based on the movement of the camera within the vehicle with respect to the road sign which is taken as the reference point. All the different stages of the system, which have been developed, form part of an experimental platform used on-board a test vehicle. To investigate the viability of this system the experimental trials have been carried out under real conditions. This methodology has been used for each stage, and the results presented corroborate the advantages and effectiveness of a multitask driver assistance platform

Driver Assistance System and Feedback for Hybrid Electric Vehicles Using Sensor Fusion

abstract: Transportation plays a significant role in every human's life. Numerous factors, such as cost of living, available amenities, work style, to name a few, play a vital role in determining the amount of travel time. Such factors, among others, led in part to an increased need for private transportation and, consequently, leading to an increase in the purchase of private cars. Also, road safety was impacted by numerous factors such as Driving Under Influence (DUI), driver’s distraction due to the increase in the use of mobile devices while driving. These factors led to an increasing need for an Advanced Driver Assistance System (ADAS) to help the driver stay aware of the environment and to improve road safety. EcoCAR3 is one of the Advanced Vehicle Technology Competitions, sponsored by the United States Department of Energy (DoE) and managed by Argonne National Laboratory in partnership with the North American automotive industry. Students are challenged beyond the traditional classroom environment in these competitions, where they redesign a donated production vehicle to improve energy efficiency and to meet emission standards while maintaining the features that are attractive to the customer, including but not limited to performance, consumer acceptability, safety, and cost. This thesis presents a driver assistance system interface that was implemented as part of EcoCAR3, including the adopted sensors, hardware and software components, system implementation, validation, and testing. The implemented driver assistance system uses a combination of range measurement sensors to determine the distance, relative location, & the relative velocity of obstacles and surrounding objects together with a computer vision algorithm for obstacle detection and classification. The sensor system and vision system were tested individually and then combined within the overall system. Also, a visual and audio feedback system was designed and implemented to provide timely feedback for the driver as an attempt to enhance situational awareness and improve safety. Since the driver assistance system was designed and developed as part of a DoE sponsored competition, the system needed to satisfy competition requirements and rules. This work attempted to optimize the system in terms of performance, robustness, and cost while satisfying these constraints.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Psychiatric Case Record

Bipolar Disorder-Mania: Patient was apparently normal one-month back, Then all of a sudden he developed sleep disturbances –mainly difficult in initiation of sleep. He also started abusing his family members for unwanted things. Subsequently, he started talking excessively and irritable. Sometimes he sings film songs and dances. He used to say that God Supreme exists in himself and so he has all the powers of Almighty. With that superior power he says that he can solve all the problems in this world. He also says that he has invented herbs to keep people young. For the past one week, he talks excessively without having an hour of sleep & wanders here and there & found excessively smoking. He becomes excessively spiritual and goes to near by villages for offering prayers to God. He takes only a little food everyday and he is very much keen in personal cleanliness. Paranoid Schizophrenia: She was apparently normal 8 months back, then she developed sleep disturbances in the form of difficult in falling asleep. She was found talking & smiling to herself at night & day with mirror gazing. She started saying that her neighbour & relatives are planning to kill herself by poisoning. In this context she had frequent quarrels with them and she refused to take food prepared by her mother in law. She left the home at night without informing any one and started wandering in the road side near her home. She was complaining that she hears voices as if her neighbour & relatives were talking about her among themselves She was not doing house hold activities for past 6 months and she was not taking care of her child. Her personal hygiene was very much deteriorated slowly as she used to take bath & brush, only if she was asked to do so. She started abusing & assaulting the strangers and family members. Generalised Anxiety Disorder: Six months back he was apparently normal. He is working as a system analyst in a private bank . He had once, made a mistake in his bank work for which he was given charges by his employer, followed this event he becomes very tense and afraid whenever his boss called him. He is very cautious that he should not commit any mistakes. Even though he is not doing so, he fears that he may commit some mistake in his work. At that moment he develops palpitation, giddiness, breathlessness, excessive sweating over palms and soles. Slowly these symptoms present through out the day even when he was not in his office, and he could not control his fearfulness. For the past 6 months he didn’t sleep well. His sleep is disturbed by bad dreams. Recurrent Depressive Disorder: Patient was apparently alright 2 months back. Then she developed sleep disturbances particularly early morning awakening, she use to wake up by 3.00 am and use to brood about herself and started crying. She was not doing her domestic work as before, as she felt excess tiredness and use to take frequent rests. She developed poor communication. She had lost her interest in pleasurable activities and was not interested in watching TV, and attending family gatherings. She stayed aloof most of the time & calm, quiet and withdrawn. She was expressing her helplessness and hopelessness about the future. She started to have decline in maintaining self care. 15 days back, she frequently expressed suicidal ideas and she had attempted suicide by hanging herself and was rescued by neighbours. 5 days back, she started talking in an irrelevant manner. She was smiling to self. She was assaulting her family members. She was suspicious that her neighbour had done black magic on her and also saying that people are talking about her. She reported hearing the voice of her neighbour scolding and threatening her. Organic Brain Syndrome – Dementia: Ten months back he was apparently alright. Then his relatives noticed himself frequently misplaces things inside his home. Then he started behaving aggressively. He was beating his wife without reason. He was roaming here and there, running out of home and wandering aimlessly. He was not able to come back home when he goes out. He was brought back to home by his relatives. Slowly he developed fearfulness and tremulousness while he was staying alone. He also started saying that his family members & neighbours were talking about himself, in this context he would make frequent quarrels with them. He also started hearing voices of known male voices abusing himself in third person. He sleeps for few hour only. He is passing urine and motion inside the house. He is asking about his brother and mother-in-law who were expired long back. He behaves abnormally such as pouring water in the plate while eating. And his relatives found the symptoms were worsened by evening. All these symptoms started insidiously, increased in severity through time and attained the present state. No history of loss of appetite / crying spells / suicidal tendencies / convulsions / fever / head injury