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XOR is a special nonlinear problem in artificial intelligence (AI) that resembles multiple real-world nonlinear data distributions. A multiplicative neuron model can solve these problems. However, the multiplicative model has the indigenous problem of backpropagation for densely distributed XOR problems and higher dimensional parity problems. To overcome this issue, we have proposed an enhanced translated multiplicative single neuron model. It can provide desired tessellation surface. We have considered an adaptable scaling factor associated with each input in our proposed model. It helps in achieving optimal scaling factor value for higher dimensional input. The efficacy of the proposed model has been tested by randomly increasing input dimensions for XOR-type data distribution. The proposed model has crisply classified even higher dimensional input in their respective class. Also, the computational complexity is the same as that of the previous multiplicative neuron model. It has shown more than an 80% reduction in absolute loss as compared to the previous neuron model in similar experimental conditions. Therefore, it can be considered as a generalized artificial model (single neuron) with the capability of solving XOR-like real problems.

Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning (MARL) can solve complex problems involving simultaneous controls. Recently, many researchers applied MARL in such applications. However, there is a lack of extensive surveys on the ongoing research to identify the current problems, proposed methods, and future research directions in MARL for CAVs. This paper provides a comprehensive survey on MARL for CAVs. A classification-based paper analysis is performed to identify the current developments and highlight the various existing research directions. Finally, the challenges in current works are discussed, and some potential areas are given for exploration to overcome those challenges. Future readers will benefit from this survey and can apply the ideas and findings in their research to solve complex problems.

Computationally intensive Inference tasks of Deep neural networks have enforced revolution of new accelerator architecture to reduce power consumption as well as latency. The key figure of merit in hardware inference accelerators is the number of multiply-and-accumulation operations per watt (MACs/W), where, the state-of-the-arts MACs/W remains several hundreds Giga-MACs/W. We propose a Tera-MACS/W neural hardware inference Accelerator (TMA) with 8-bit activations and scalable integer weights less than 1-byte. The architectures main feature is configurable neural processing element for matrix-vector operations. The proposed neural processing element has Multiplier-less Massive Parallel Processor to work without any multiplications, which makes it attractive for energy efficient high-performance neural network applications. We benchmark our systems latency, power, and performance using Alexnet trained on ImageNet. Finally, we compared our accelerators throughput and power consumption to the prior works. The proposed accelerator outperforms the state of the art in terms of energy and area achieving 2.3 TMACS/W@1.0 V, 65 nm CMOS technology.

Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.

Fully autonomous vehicles (FAVs) lack monitoring inside the cabin. Therefore, an in-cabin monitoring system (IMS) is required for surveilling people causing irregular or abnormal situations. However, monitoring in the public domain allows disclosure of an individual’s face, which goes against privacy preservation. Furthermore, there is a contrary demand for privacy in the IMS of AVs. Therefore, an intelligent IMS must simultaneously satisfy the contrary requirements of personal privacy protection and person identification during abnormal situations. In this study, we proposed a privacy-preserved IMS, which can reidentify anonymized virtual individual faces in an abnormal situation. This IMS includes a step for extracting facial features, which is accomplished by the edge device (onboard unit) of the AV. This device anonymizes an individual’s facial identity before transmitting the video frames to a data server. We created different abnormal scenarios in the vehicle cabin. Further, we reidentified the involved person by using the anonymized virtual face and the reserved feature vectors extracted from the suspected individual. Overall, the proposed approach preserves personal privacy while maintaining security in surveillance systems, such as for in-cabin monitoring of FAVs.

The purpose of this research is to design a simple and intuitive path tracker to minimise the distance error of a car-like delivery service robot using only a position sensor, i.e. global positioning system in outdoor environments. The authors proposed not only a single-point preview path tracker (SPP), but also a curvature-based preview control using a quadratic curve fitting technique. This method can provide flexibility in preview distance control even at a constant speed, and reduces the burden of an actuator for maintenance. Furthermore, the performance of the proposed adaptive SPP is considerably improved compared to that when using a fixed preview distance.

We propose an artificial deep neural network- (ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-the-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle’s behavior resemble a real vehicle.

We propose a noninvasive method for determining Langer’s tension lines and tracking two-dimensional (2D) deformation of human skin utilizing spatially resolved diffuse reflectance (SRDR) of infrared (IR) light measurement in vivo. The anisotropic light propagation pattern of the skin was measured at several locations on the human body; then, the principal axis (i.e., eigenvector) of the radial plot of equi-distant intensity lines was obtained to determine the orientation of the scattering-induced anisotropic reflectance of IR light. The results indicate the proposed method is able to determine preferential orientation of filamentous fiber with a reasonable degree of prediction accuracy over the entire body. We validated the proposed method by comparing the detected orientations of fiber with a previous scattering model-based light propagation anisotropy analysis method. We measured temperature dependence and tensile stress-induced changes of the spatially resolved reflectance of IR light on the skin. The surface temperature affects the light transmittance almost isotopically on the propagation patterns. The main contribution of this work is tracking the direction of tension causing 2D deformation of human skin without visible markers. The proposed method is applicable to sensing facial gestures or skin movements induced by human motion of skeletal or facial muscles.

Absence of human in-charge in fully autonomous vehicles (FAVs) impose multi-pronged in-cabin monitoring in real-time to monitor any irregular situation. Moreover, occupant’s visual information (such as video, images) transmission from in-cabin of vehicle to the data center and control-room is required for various in-cabin monitoring tasks. However, this information transfer may cause a substantial threat to the occupants. Though applying some patch on face of the occupants protects personal information however, simultaneously it deteriorates the important facial information which is very crucial in monitoring various tasks such as emotion detection. Therefore, a human-computer-interaction (HCI) is required to manage this trade-off between facial information and facial anonymization without any important information loss. Also, this HCI based in-cabin monitoring system should be implicit to retain the users trust in the intelligent transportation system (ITS). In this paper, we have proposed generative adversarial network (GAN) based in-cabin monitoring approach for emotion detection in FAV. In this method, we proposed to generate an artificial (virtual) face having real-facial expressions to provide facial anonymity to the occupants while monitoring FAV cabin. Therefore, this method provides anonymous facial information which is essentially required in detection and monitoring tasks to avoid irregular situations. We have used both publicly available and our own in-cabin dataset to assess our proposed approach. Our experiments show satisfactory results.

Recent progress in parallel computing machines, deep neural networks, and training techniques have contributed to the significant advances in artificial intelligence (AI) with respect to tasks such as object classification, speech recognition, and natural language processing. The development of such deep learning-based techniques has enabled AI-based networks to outperform humans in the recognition of objects in images. The graphics processing unit (GPU) has been the primary component used for parallel computing during the inference and training phases of deep neural networks. In this study, we perform training using a desktop or a server with one or more GPUs and inference using hardware accelerators on embedded devices. Performance, power consumption, and requirements of embedded system present major hindrances to the application of deep neural network-based systems using embedded controllers such as drones, AI speakers, and autonomous vehicles. In particular, power consumption of a commercial GPU commonly surpasses the power budget of a stand-alone embedded system. To reduce the power consumption of hardware accelerators, reductions in the precision of input data and hardware weight have become popular topics of research in this field. However, precision and accuracy share a trade-off relationship. Therefore, it is essential to optimize precision in a manner that does not degrade the accuracy of the inference process. In this context, the primary issues faced by hardware accelerators are loss of accuracy and high power consumption.

Advances in the architecture of inference accelerators and quantization techniques of neural networks allow effective on-device inference in embedded devices. Privacy issues for user data, as well as increasing needs of user-specific services, have led to a need for on-device training. The dot product operation required in backpropagation can be computed efficiently by multiplier–accumulators (MACs) in the inference accelerator if forward and backward propagation of the neural network have the same precision. This chapter introduces a quantization technique to enable computation by the digital neuron inference accelerator with the same precision as that using the forward path. Updating the 5-bit weights with gradients of higher precision is challenging. To address this issue, this chapter also introduces a probabilistic weight update. It also describes the hardware implementation of the probabilistic weight-update scheme. The proposed training technique achieves 98.15% recognition accuracy on the MNIST dataset.

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

We propose a novel type of near-infrared (NIR) based sensory system for the detection of skin deformation induced by facial expressions. The proposed system utilizes the diffusion property which was called spatially resolved diffuse reflectance of NIR light in human skin as clues of detecting skin deformations. The sensor was mainly composed of NIR laser diode and a camera as a light source and receiver. To refine the information from captured IR raw images, we exploited principal component analysis based preprocessing procedure. By producing sensor data collecting experiment on several participants, we've verified the facial deformation tracking performance of the proposed sensory system.

Intelligent gas monitoring system is having its widespread applications. It essentially requires an accurate classification with precise quantification of gases/odors. Although, gas sensor arrays are capable of generating signatures however, mostly these signatures are complex and have subtle information. Therefore, in this paper, an efficient sensory system has been proposed for intelligent gas monitoring. Concept of analysis space transformation has been utilized for accurate classification of gases/ odors. Also, the drawback of data preprocessing in quantification has been illustrated by comparing the quantifier performance for the processed and raw responses. Further, the proposed sensory system has elevated the classification accuracy from 96% to 98.74% along with the quantification accuracy from 17.65% to 94%.

Absence of human driver inside fully autonomous vehicle (FAV) essentially requires monitoring the in-cabin of such transportation systems. Although, previous researchers claimed inside monitoring of vehicle. However, mostly they have performed diver status monitoring. It is quite insufficient for FAV, and proper in-cabin monitoring is an essential demand to ensure safety and security to both, human and vehicle. In this paper, we have proposed a single artificial intelligence (AI) based camera to monitor and understand in-cabin occupants and their behaviors. Further, requirements of monitoring inside the vehicle cabin has been showcased and various irregular situations have demonstrated which should be considered to provide safe and secure ride to the occupants. Furthermore, we have developed our own database due to very few availabilities of such used cases and dataset. It is to facilitate more research in this domain.

We live in a world that is getting more interconnected by each day, and we are witnessing a global change where all the devices in our surroundings are becoming “smart” and connected to the Internet. The automotive industry is also a part of this change. Today’s vehicles have more than 150 small computers, embedded control units (ECUs), and multiple connection points to the Internet which makes them vulnerable to various online threats.

The Fourth Industrial Revolution will see autonomous vehicles at the center. The automotive industry has already become a worldwide issue that is not constrained solely by one country’s strategy.

In this chapter, we will discuss the emerging mobility ecosystem and its potential growth that enhance the vehicle movement from source to the destination. This helps to save the passenger’s time and provides comfort during traveling. The main objective of the future of mobility along with the autonomous transportation and shared mobility is to overcome the predictable accidents on the road. The vehicles have evolved from mechanical transportation means to the smart vehicles with varieties of communication and sensing capabilities. With the development of Internet of Things (IoT), the conventional vehicles have developed over time and turned to Internet of Vehicles (IoV) with advanced connectivity with Internet, infrastructures, context awareness, sensing capabilities, service provisioning, etc. The IoV has developed over time from the conventional vehicular networks that connect the smart vehicles to the smart city. The IoV is a multifaceted vehicular network, where the vehicles consist of various sensors installed that gathers data from other vehicles and road infrastructures. IoV is a special case of IoT, which is developed specially for automotive vehicles. Several applications of IoV are indispensable for vehicles, drivers, pedestrian, smart city infrastructure, etc. The IoV has been popular among the automotive industry research, and they are attracted to the academic sector as well. In IoV, a large number of vehicles interconnect with each other and they communicate through heterogeneous wireless communications. We discuss seven-layered architecture of IoV in detail. The seven layers of IoV are interface layer, data acquisition layer, data preprocessing layer, communication layer, management and control layer, processing layer, and the security layer. In IoV, there exists congestion and scalability issue due to the increase in the number of vehicles and are not able to preprocess huge amount of collected data from various sensors and neighbor vehicles. The IoV requires serious attention, and the security in the IoV needs to be studied carefully to prevent from serious cyber-attacks. The existing IoV architecture lacks security requirements such as authentication, authorization, and trust-related issues. We discuss several types of attacks in IoV and challenging issues in IoV. Then, we mentioned three types of IoV applications in intelligent transportation system (ITS), business-related applications, and smart city applications.

In this chapter, we focus on the connected vehicle technology and describe in detail about the different types of connected vehicle technology and its security issues. The intelligent and autonomous vehicle (IAV) technologies are expected to revolutionize the intelligent transportation system internationally.

This chapter gives a brief description of the main topics of the book in automotive cybersecurity. The automotive industry, which comprises many companies and organizations, is one of the leading industries in the world as it is more aware of its environment and responds to it. The history of intelligent autonomous vehicles has improved more than two decades before. Ever since, the automotive industry has made a big transformation to ensure efficiency and safety by eliminating traffic accidents for both the drivers and passengers. The technological progress made in sensor and navigation systems, the Internet of Things, and different types of machine learning techniques would promote new, innovative, and accessible mobility that gives rise to intelligent and autonomous vehicles. However, security plays a vital role in intelligent and autonomous vehicles. In this book, we discuss in detail about the different levels of autonomous vehicles from Level 0 to Level 4, different types of cybersecurity issues in autonomous vehicles, and future trends and challenges in autonomous vehicles. Security must be thought as an important aspect during designing and implementation of the autonomous vehicles to prevent from numerous security threats and attacks. The purpose of this chapter is to provide a comprehensive overview of automotive connectivity and to provide a framework for discussion of the several challenges and issues related to automotive connectivity, security from a technical perspective.

In this chapter, we mostly focus on cybersecurity and privacy in intelligent and autonomous vehicles (IAV). This chapter starts with the basics of cryptography and then proceeds to different types of advanced security and encryption schemes that can be used in autonomous vehicles. The cyber security in intelligent and autonomous vehicles can be a combination of physical security, information security, security elements, policies, standards, legislation, and risk mitigation strategies. We introduced the updated cybersecurity framework that provides a specific categorization and structural framework for institutions to describe their current cybersecurity position, state for cybersecurity, identify and prioritize security improvements, assess security progress, and plan concerning cybersecurity risks. Then, we discuss about the five key technological cybersecurities to protect any company, organization, and IAV against a cyber-attacks. A threat modeling method (TMM) is also required to investigate the potential threats so that the IAV system is fully secured from unknown attacks. The TMM is used to defend the cyber-physical system from attackers and detect the threats before they create severe damage. Some of the examples of TMM are STRIDE, PASTA, VAST, etc. The vulnerability is the weak point in the scheme that is misused by the malicious attacker in the form of attacks for their own advantages. We discuss some of the taxonomy that can be found in vehicular system such as autonomous vehicle vulnerability taxonomy, defense taxonomy, and privacy taxonomy.

The increasing urbanization demands development of smart cities. Smart cities can be considered as to serve the requirement of its citizen in better way. The smart cities have many applications involving intelligent gas monitoring. In this chapter we will find about the intelligent gas monitoring in smart city scenario. We will see the aspects of smart gas monitoring, understand the concept of gas sensing, requirement of gas monitoring viz., classification and quantification of gases/odors and the brief introduction about the gas sensing. Further, we will understand the application of blockchain in the intelligent gas monitoring.

This chapter provides an overview of the developments in intelligent and autonomous vehicle (IAV) that accompany in-vehicle and inter-vehicle connectivity. This chapter gives detailed information regarding the evolution of intelligent and autonomous vehicles, its classification based on vehicle driving levels, associations between connectivity and autonomy for vehicle performance and the state-of-the-art applications.

Intelligent vehicle (IV) is an internet-enabled vehicle, commonly referred to as a self-driving car, which enables vehicles-to-everything communications. This communication environment is not secure and has several vulnerabilities. The major issues in IV communication are trustworthiness, accuracy, and security of received and broadcasted data in the communication channel. In this article, we introduce blockchain technology to build trust and reliability in peer-to-peer networks with topologies similar to IV communication. Further, we propose a blockchain-technology-enabled IV communication use case. Blockchain technology is used to build a secure, trusted environment for IV communication. This trusted environment provides a secure, distributed, and decentralized mechanism for communication between IVs, without sharing their personal information in the intelligent transportation system. Our proposed method comprises of a local dynamic blockchain (LDB) and main blockchain, enabled with a secure and unique crypto ID called intelligent vehicle trust point (IVTP). The IVTP ensures trustworthiness among vehicles. Vehicles use and verify the IVTP with the LDB to communicate with other vehicles. For evaluation, we simulated our proposed blockchain technology-based IV communication in a common intersection deadlock use case. The performance of the traditional blockchain is evaluated with emphasis on real-time traffic scenarios. We also introduce LDB branching, along with a branching and un-branching algorithm for automating the branching process for IV communication.

Comfort driving has emerged as an important topic in the autonomous car research field. This study focuses on lane change maneuvering (LCM) of autonomous vehicles to provide a comfortable driving experience for passengers. For this purpose, we propose an LCM algorithm for determining a desired trajectory by evaluating the allowable lateral acceleration value obtained from Bezier curves at a local path planning stage for comfortable and smooth motion of the vehicle. The performance of the proposed LCM algorithm was verified through computer simulations and real driving tests.

Developing a user interface (UI) suitable for headset environments is one of the challenges in the field of augmented reality (AR) technologies. This study proposes a hands-free UI for an AR headset that exploits facial gestures of the wearer to recognize user intentions. The facial gestures of the headset wearer are detected by a custom-designed sensor that detects skin deformation based on infrared diffusion characteristics of human skin. We designed a deep neural network classifier to determine the user&rsquo, s intended gestures from skin-deformation data, which are exploited as user input commands for the proposed UI system. The proposed classifier is composed of a spatiotemporal autoencoder and deep embedded clustering algorithm, trained in an unsupervised manner. The UI device was embedded in a commercial AR headset, and several experiments were performed on the online sensor data to verify operation of the device. We achieved implementation of a hands-free UI for an AR headset with average accuracy of 95.4% user-command recognition, as determined through tests by participants.

With continuously changing operational and business needs of the organizations, Decentralized Autonomous Organizations (DAO) is the current need of the organizations. Centralized Autonomous Organization (CAO) lack transparency and are managed by few efficient managers whereas Decentralized autonomous Organization's (DAO) is novel scalable, self-organizing coordination on the blockchain, controlled by smart contracts and its essential operations are automated agreeing to rules and principles assigned in code without human involvement. In this chapter we discuss the needs for Decentralized Autonomous Organizations (DAO) and key efforts in this field. We then introduce a prospective solution employing blockchain Ethereum, which incorporates a Turing complete programming language with smart contract computing functionality. A solution is elaborated that permits the formation of organizations where participants preserve straight real-time check of contributed collects and governance policies are formalized, automatized and imposed using software. Basic code for smart contract is composed to make a Decentralized Autonomous Organization (DAO) on the Ethereum blockchain. We also explain the working of DAOs code, centering on fundamental establishment and governance characteristics, which includes organization, formation and voting rights. DAOs are considered to agree to the expectation of the business work in the future. But there is still lack of operational base for DAOs in the blockchain community.

The advancement of new technology has taken a huge leap in the last few decades. It is bringing a drastic change in each step of human life that are capable of performing intelligent tasks. The internet of things and blockchain are disruptive technologies that have received a huge attention from industry, academic and financial technologies. There is a risk of privacy leakage of sensitive information in the centralized IoT system because the centralized servers can access the plain text data from the IoT devices. There is an extensive interest in applying the blockchain in the IoT system to provide IoT data privacy and decentralized access model. However, the previous blockchain-based IoT systems have issues related to privacy leakage of sensitive information to the servers as the servers can access the plaintext data from the IoT devices. So, we present the potential of integration of blockchain based-IoT with homomorphic encryption that can secure the IoT data with high privacy in a decentralized mode. In addition, we provide comparison of the recent technologies toward the securing and preserving the privacy of the IoT data using blockchain and homomorphic encryption technology. We have also highlighted the research challenges and possible future research directions in the integrated blockchain-based IoT with homomorphic encryption.