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The cars we drive are rapidly transforming. Connected vehicles in the context of the Advanced Driver Assistance System or Autonomous Vehicles are about to change the way we drive cars. Connected Vehicles are futuristic vehicles that can interact with other vehicles for passing on information such as, mapping and localization, information about road traffic and driving behaviour. However, such vehicles, particularly the autonomous ones, are prone to a variety of attacks including cyber-attacks. These malicious attacks can intrude a vehicle that not only endangers the vehicles safety, but also the life of passengers and the nearby environment. Thus, identifying and eliminating these attacks for providing a secure communication environment is of great need. Also, all the existing methods for vehicular communication rely on a centralized server which itself invite massive cyber-security threats.

The automotive industry is set for a rapid transformation in the next few years in terms of communication. The kind of growth the automotive industry is poised for in fields of connected cars is both fascinating and alarming at the same time. The communication devices equipped to the cars and the data exchanges done between vehicles to vehicles are prone to a lot of cyber-related attacks. The signals that are sent using Vehicular Adhoc Network (VANET) between vehicles can be eavesdropped by the attackers and it may be used for various attacks such as the man in the middle attack, DOS attack, Sybil attack, etc. These attacks can be prevented using the Blockchain technology, where each transaction is logged in a decentralized immutable Blockchain ledger. This provides authenticity and integrity to the signals. But the use of Blockchain Platforms such as Ethereum has various drawbacks like scalability which makes it infeasible for connected car system.

Due to the rapid development in the technological aspect of the autonomous vehicle (AV), there is a compelling need for research in the field vehicle efficiency and emission reduction without affecting the performance, safety and reliability of the vehicle. Electric vehicle (EV) with rechargeable battery has been proved to be a practical solution for the above problem. In order to utilize the maximum capacity of the battery, a proper power management and control mechanism need to be developed such that it does not affect the performance, reliability and safety of vehicle. Different optimization techniques along with deterministic dynamic programming (DDP) approach are used for the power distribution and management control. The battery-operated electric vehicle can be recharged either by plug-in a wired connection or by the inductive mean (i.e. wirelessly) with the help of the electromagnetic field energy.

From the recent advances in Driver Monitoring Systems (DMS) from automotive domain, research on Human Computer Interaction (HCI) based on emotion analytics has gained good interest from the research circles. Distraction and drowsiness will be causing more percentage of traffic accidents, but with the use of advanced DMS technology, we can significantly reduce these distractions and can make the driving a safer activity. Our proposed solution/approach with disguised emotion detection with analytics is enabled by machine learning and image processing algorithms to ensure that the detection of drowsiness or distraction is very accurate. The proposed method will inform the HMI system to provide an alert to wake up the driver if he or she is in drowsy state or take the proactive/necessary actions with the help of active safety systems. Emotion analytics is a technique which is used to analyze the emotion of an individual. It is used to recognize the change in the emotion.

In recent years, research on Human Computer Interaction (HCI) based on emotion recognition using behavioral and physiological signals have attracted immense interest in research circles. Lighting inside the automotive make us feel differently about our driving and how we feel or behave. From the literature, it is observed that ambient lighting makes an impact on the driving experience and it delivers an emotional atmosphere inside the automotive. Driving fatigue can be reduced if the lighting is controlled properly. These days, ambient interior lighting can be considered to be the point of fashion for high end automotive and also impact driver’s mood and comfort. There are different types of automotive based lighting automation systems available but emotion based control is in early or nascent stages of research. Speech controlled light control systems, control the light by the recognition of speech of the user and by using facial expressions lighting can be controlled.

In the food industry, there is an increased demand for generic pharmaceutical products and perishable food without compromising with the changes in texture and taste that occur in the transit. With this demand, there is a need for better visibility of products in the logistics network, to minimize wastage, to ensure product integrity, influence productivity, transparently track the fleet and to identify pathogens before a potential outbreak. In Cold Chain Management, information is power: with potentially billions of dollars’ worth of cargo (such as food items, vaccines, serums, tests or chemicals) at stake worldwide. Hence, careful live monitoring, inspection, supervision, validation and documentation of business-critical information is essential.

Accidents in nights have a major share in all automotive accidents. Even though, the average distance driven in dark is 75% less as compared to the average distance driven during the day, the fatalities in nights due to road accidents are 300% of the day time. Again, the statistical studies from the National Safety Council disclose the fact that 55% of all road accidents in nights occur at the curved roads due to insufficient illumination and poor judgment of curves. The paper proposes a control algorithm with machine learning that controls LED matrix headlamp to provide precise beam pattern shaping and beam intensity (i.e. high and low beam). The system is designed to give the driver improved visibility under varying driving conditions. Adaptive Front Lighting System is an intelligent system, designed in MATLAB\Simulink environment that optimizes the illumination of roads during the night, on the basis of inputs from different sensors in the vehicle.