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Über dieses Buch

This book is a compilation of the recent technologies and innovations in the field of automotive embedded systems with a special mention to the role of Internet of Things in automotive systems. The book provides easy interpretable explanations for the key technologies involved in automotive embedded systems. The authors illustrate various diagnostics over internet protocol and over-the-air update process, present advanced driver assistance systems, discuss various cyber security issues involved in connected cars, and provide necessary information about Autosar and Misra coding standards. The book is relevant to academics, professionals, and researchers.



Automotive Safety Systems

Over the past century, after the slew of technological revolutions and advancements, the need for safe transportation of goods and people over a distance at higher speed has become an important part of the living and in particular, with the progression. With major advancements in the automotive industry, from the very first car that clocked just 10 miles per hour to the latest electric and autonomous vehicles, personal transportation has seen an exponential growth. With the number of vehicles and users increasing day by day, the safety and security aspects of the vehicles have become inevitable. Every step towards safer and secure practices requires many technological changes based on the past study and observations; thus, keeping in mind the needs and demands of the modern population, automakers all over the world are focusing on these features of the vehicles.
Two very interdependent as well as significant properties of automobiles are safety and security. Vehicles need to be protected from accidental failures as well as premeditated attacks as they can easily lead to catastrophic consequences. This chapter discusses various radical and essential concepts pertaining to safety and security of automotive systems.
S. Hamsini, M. Kathiresh

Virtualizing an Automotive State-of-the-Art Microcontroller: Techniques and Its Evaluation

The future of automotive industry has a major focus on computing innovations such as autonomous driving, connectivity, and mobility. With these advancements, electrical and electronic components started exponentially increasing inside the vehicle, integrating hardware and software components of different automotive safety integrity levels. Automotive OEMs and their suppliers are seeking for innovative and optimized electrical/electronic vehicle architecture to improve vehicle performance, safety, reliability, and lower system costs. Consolidating several small heterogeneous computing units to a centralized computing unit is an approach for optimizing the electrical/electronic vehicle architecture. A demonstrator virtualizing an automotive state-of-the-art multicore controller with two heterogeneous hard real-time applications is realized. Through this demonstrator, various new concepts like start-up of virtualized system, trap-emulation, virtualizing input–output access, and interrupt handling are realized. These concepts are validated in terms of performance, data consistency, memory consumption, timing to its deadlines, and reliability of the system. Compared to the research works done so far, this evaluation is based on a demonstrator where both virtualized applications are performing their regular system activities. There is no master-slave concept in this demonstrator, enabling independent access for each application to its needed peripheral.
Arun Kumar Sundar Rajan, M. Nirmala Devi

AUTOSAR and MISRA Coding Standards

The recent times are witnessing the extension of the field of electronics into the field of automobiles. This resulted in the introduction of the Electronic Control Units into the automotive industry and the need for the development of the automotive software for these units to operate. Hence, AUTomotive Open System ARchitecture (AUTOSAR), a consortium, was formed by the automotive partners who developed standardized software so that the automotive companies could comply with it and the process of the development of a software] right from the beginning, every time, could be eliminated.
The automotive industry deals with real-time systems. So the objective must be attained with precision, and failing to do so will result in catastrophes which put human lives and the environment in danger. In order to obtain the results in precision, the method of asking, i.e., the coding of the software in development, should be in such a way that the outputs should be predictable and any chance leading to the unpredictable behavior should be eliminated. To make this happen, MISRA, the Motor Industry Software Reliability Association, a consortium, was formed from the representatives of the companies of the automotive industry to set up coding guidelines that are to be followed in the development of an embedded software.
This chapter discusses the layered architecture of AUTOSAR and the overview of MISRA coding guidelines. Besides this, it also briefly explains few rules of MISRA under different categories with supporting examples.
Y. Catherine Yamili, M. Kathiresh

Model-Based Automotive Software Development

In recent years, the need for better comfort and safety in automobiles leads to the drastic increase in the number of electronic control units in automobiles. These extra features in a vehicle increase the complexity of the software in ECUs. Model-Based Software Development (MBSD) is widely used in automotive software development in order to reduce the cost and time meanwhile ensuring the safety of the product. This chapter explains the concept of MBSD in the automotive domain. This chapter also discusses the tools and techniques available in the market for Model-Based Software Development with provision of auto code generation. MATLAB/Simulink/Stateflow-based modeling approach is described with a brief introduction to Unified Modeling Language.
K. Vinoth Kannan

Vehicle Diagnostics Over Internet Protocol and Over-the-Air Updates

Due to the major advancements in automotive electronic systems and wireless communication technology, processes such as vehicle diagnostics and updating the software in vehicle Electronic Control Units are possible from a remote location through the Internet without having vehicles being present at the service stations. This chapter discusses the process of vehicle diagnostics over Internet protocol and software updates over the air. This chapter also deals with the various security issues involved in these processes and provides an overview to the various techniques that tackle data security issues in vehicles.
M. Kathiresh, R. Neelaveni, M. Adwin Benny, B. Jeffrin Samuel Moses

Automotive Cybersecurity

In recent years automobiles have undergone major changes from a mechanical machine to a machine having high level of electronics and software. Automobiles are also connected over networks and to the Internet to enable some of the vehicular applications. This opens up threats for cyberattacks, and automotive cybersecurity is a very important area not only for study purpose but also for design and engineering of modern vehicles. This chapter gives an introduction to this exciting field of automotive cybersecurity. It refers to the relevant recent standards and technologies that are evolving in this area.
Ashish Jadhav

Autonomous Vehicles: Present Technological Traits and Scope for Future Innovation

Earth can soon witness cars that will drive themselves without any drivers, i.e., independently. In near future, the car driving will be controlled by the autonomous driving systems technology, which will adjust the speed of the vehicle and run miles together without any glitches on all types of roads.
The advancement in technology is accelerating the growth of autonomous driving. Autonomous vehicles (AVs) are the latest trend that the car industry is moving toward, as AVs will help in replacing humans as drivers who are prone to making mistakes and errors while driving the vehicle. Thus having AVs on roads will reduce the number of accidents caused due to the human error and hence serve as a means to improve road traffic safety. However, AVs face innate safety and security challenges that must be addressed before they are deployed for use.
The complex hardware design required to accommodate autonomous systems is shrinking, whereas we can see a sudden increase in the processing speeds. Advanced driver assistance systems (ADAS) are readily used in buses that have amazing features such as pedestrian detection system, adaptive cruise control, collision avoidance, correction of lane, and automated parking facility that can be used in the design of AVs as well.Every novel feature that you want to integrate into the AVs such as smartphone integration, entry without keys, and detection of blind spots in the next-generation vehicles brings new vulnerabilities and challenges to the electronic control unit (ECU).
Even with all the research going on, to make driverless cars a success, the autonomous industry is facing its own set of challenges with respect to the technical implementation of driverless cars such as decision-making, actuation, sensor management, object detection efficiency, safety and reliability, quality of software, computational resources, security and hacking, and privacy, being the essential ones. There is still a long way to go for the industry till it actually reaches its ultimate destination. So this chapter dives into the current state of autonomous driving, the various stages, and the key technological challenges that the vehicle manufacturers must address to get in the game.
Arun S. Tigadi, Nishita Changappa, Shivansh Singhal, Shrirang Kulkarni

Artificial Intelligence and Sensor Technology in the Automotive Industry: An Overview

Recently, artificial intelligence (AI) has contributed a key role in the field of automotive industry in the form of self-driving cars or automated vehicles (AV) with innovative features. The automotive industry is driven by various potential technologies such as sensor technology, communication techniques, machine learning and deep learning algorithms. Using AI, a lot of innovative products and applications have been developed in the automotive industry and they have reduced most of the human errors such as aggressive driving, accidents and traffic collisions, etc. This article explores AI-based applications in the automotive industry and also discusses relevant algorithms behind this new era of AV. Moreover, this article investigates the role of sensors and actuators which are the prime requisite of building an AV. This also discusses the challenges involved in the AV.
S. Meenakshi Ammal, M. Kathiresh, R. Neelaveni

Advanced Driver Assistance Systems (ADAS)

Advanced driver assistance systems (ADAS) refer to technologies that automate, facilitate, and improve systems in the vehicles in order to assist drivers for better and safer driving. There are several ADAS technologies such as adaptive cruise control (ACC), lane departure warning systems, forward collision warning systems, traffic signal recognition system (TSR), tire pressure monitoring system (TMPS), night vision, pedestrian detection, parking assistance systems, automatic emergency brake systems, driver behavior monitoring, blind spot detection, electronic stability control (ESC), alcohol interlock systems, etc. Some of the ADAS technologies are intended for safety improvement, and some others are for convenience function. This chapter explains each of the different ADAS technology in detail with their deployment details. The development and deployment of these technologies relies mainly on the embedded systems and advanced signal processing technologies such as multiple signal classification (MUSIC) and light detection and ranging (LiDAR).
The main focus of the ADAS technologies is to contribute to the factors such as safety management and stress-free automated driving for a driver. In order to enable these ADAS technologies, a suite of sensors is essential. There are different types of sensors being used similarly, i.e., vision sensors, LiDAR sensors, RADAR sensors, ultrasonic sensors, and other technologies such as photonic mixer device (PMD) and global positioning sensor (GPS). The vision-based sensors take the decisions based on the images acquired. The images acquired are pre-processed for the image processing and segmented to find various features in the image. The segmented images are used for identification and classification based on various machine learning algorithms and neural networks. Another concept to be discussed is regarding the NEXT-GEN ADAS, where the sensor suite together is used with advanced communication technologies such as vehicle-to-everything (V2X) communication. In other words, ADAS is a pathway and major contribution towards autonomous driving. There are several challenges that need to be addressed associated with ADAS technologies related to changing environmental conditions, resource-constrained systems, and security and geospatial constraints. This chapter will be covering the description regarding the above topics with detailed diagrams and descriptions.
Maria Merin Antony, Ruban Whenish

Analysis of IoT-Enabled Intelligent Detection and Prevention System for Drunken and Juvenile Drive Classification

Drunken driving and juvenile driving are the root causes of accidents on the road. The aim of this book chapter is to put an end to the cause of such accidents, with the use of an IoT-enabled smart automobile by preventing drunken and juvenile drivers from accessing the automobile. A survey highlights that between 2008 and 2017, drunken driving and use of drugs has led to 211,405 accidents across India resulting in the death of 76,446 people. Data also indicates that 2317 juvenile drivers died in accidents during the year 2018. Our solution is to curb the problem at the root, by preventing the driver from accessing the automobile when they are in an intoxicated state or are juvenile. A graphene sensor is fitted on the steering wheel of the automobile. The driver will have to blow air on the sensor; depending on the result, the driver will be given access/denied permission to start the automobile. A fingerprint sensor will also be installed along the rims of the wheel which in turn will fetch data from the cloud and check the age of the driver who is driving once every 30 min. The graphene sensor and the fingerprint sensor are interfaced with the Microcontroller FRDM-K64F which is linked to the cloud-stored database. When the graphene sensor and the fingerprint sensor give permission, the automobile can be started and driven.
D. Ruth Anita Shirley, V. Kamatchi Sundari, T. Blesslin Sheeba, S. Sheeba Rani

Internet of Things and Artificial Intelligence-Enabled Secure Autonomous Vehicles for Smart Cities

The ever-increasing count of vehicles wrecks several cities in the global scenario. Smart cities have evolved as a winning strategy that helps to cope up with this issue and overcome the urban problems such as pollution, traffic, waste management, optimization of energy consumption, and so on. Technologies such as machine learning (ML), Internet of Things (IoT), artificial intelligence (AI), big data analytics, cloud computing, and smart sensors serve as tools that provide enormous possibilities in the smart revolution. Several researchers are working on developing a complete system that performs information gathering, alternate identification, smart predictions, review of choices, decision-making, and taking suitable actions. These systems impose various challenges in terms of governance, economy, mobility, environment, people, and living. This chapter provides an in-depth analysis of these challenges in smart cities with respect to autonomous vehicles and also offers real-time solutions to overcome these challenges. A comparative analysis of the existing algorithms is done, and the optimal algorithms that can help in implementation of the system with a user-friendly approach and linguistic flexibility are proposed. Factors such as use of unmanned aerial vehicles (UAV), vehicle-to-vehicle and vehicle-to-infrastructure communication, deployment of location and path planning, data routing, dynamic coordination, data transmission, privacy, and cybersecurity are also considered while designing the system.
D. A. Janeera, S. Sheeba Rani Gnanamalar, K. C. Ramya, A. G. Aneesh Kumar


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