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Autonomous vehicles or self-driving cars and semi-autonomous cars provide numerous driving assistance to the driver and passengers. However, with the advancements in driving technology the driver’s experiences and preferences are also taken into consideration for achieving the advanced safety goals. The drivers’ comfort and experiences are considered with the design and development of modern-day vehicles makes the driver’s preferences crucial for providing the driving experience. During the process of driving, the experience received by the driver is associated with the functional safety of the automotive system and hence the experience should be smooth with respect to safety. In this regard, the personalization in the space of the driver assistance system is gaining importance in analysing the driver’s behaviors and providing personal driving experiences to the driver.

Automotive system functionalities spread over a wide range of sub-domains ranging from non-driving related components to complex autonomous driving related components. The requirements to design and develop these components span across software, hardware, firmware, etc. elements. The successful development of these components to achieve the needs from the stockholders requires accurate understanding and traceability of the requirements of these component systems. The high-level customer requirements transformation into low level granularity requires an efficient requirement engineer. The manual understanding of the customer requirements from the requirement documents are influenced by the context and the knowledge gap of the requirement engineer in understanding and transforming the requirements.

Vehicular odometers serve as a standard component in driver assistance system to provide continuous navigation. Odometer fraud is the disconnection, resetting, or alteration of a vehicle’s odometer with the intent to change the number of miles indicated. Odometer fraud occurs when the seller of a vehicle falsely represents the actual mileage of a vehicle to the buyer. But the Odometer readings are essential when it comes to ascertaining the fair market value of a used vehicle. Hence, there is a need to protect the odometer which resides in the instrument cluster of the digital cockpit. Any manipulation is very difficult to detect and to prove once made, even by expert technicians using specific On-Board Diagnostics (OBD) testing devices. One of the most critical issues is that currently odometers are not locked out from external access, in contrast to other vehicle components, which have higher protection levels.

An emergency vehicle is one of the critical vehicles designed by the Original Equipment Manufacturers (OEMs) to support the emergency assistance and maintenance for different emergency situations such as fire, health, etc. These vehicles are provided a special provision by the legal authorities to bypass the road traffic scenarios and guidelines. The special provisions mainly include a co-operation of the non-emergency vehicles on the road for providing a smooth provision for the emergency vehicles to move, bypassing of traffic signals, etc. Sometimes, the intruders utilize these provisions to hide their original identities by utilizing the emergency vehicles for their transportation. The vehicles utilized by intruders for this purpose are the illegitimate ones, which are carried by attackers for illegal purposes, but they possess the same external appearances and special alerting system as the legitimate emergency vehicles.

The advancements of the automotive system in all the aspects from safety to user experience brings never ending list of electronics components into the system. One of the pure critical components in providing the vehicle safety is the digital key or wireless vehicle entry systems. This component is responsible for protecting all the other components of the vehicle and the vehicle itself from thieves and illegal usage of the vehicle. The compromisations of this critical component is equivalent to a compromisations of the entire vehicle along with some legal implications on the vehicle owner. There are numerous additional systems in automotive electronics which enhances the security of the critical, digital key/wireless vehicle entry system in protecting the vehicles from attackers. However, there is no component available in the market which does user/owner authentication considering its impact and criticality on both the vehicle and its owner.

Autonomous vehicles are waiting to address the global automotive mobility challenges through an intelligent smart transportation system, which includes advanced sensor-actuator configurations to control, navigate, and drive the vehicles. Multi-sensor data fusion from the key sensors such as camera, radar, and lidar is used to achieve the environmental perception for autonomous vehicles by capturing the various attributes of the environment. Cameras are the dominant sensors to achieve the perception by providing vision capability to vehicles. The direct interface of the cameras with the dynamic driving environment carries numerous attack surfaces on the camera. Blinding attacks on the cameras are one of the critical attacks with an intention to blind the cameras either fully or partially by projecting light into the cameras to hide the objects which results in failure in object detection.

Modern vehicles are increasingly equipped with multiple advanced on-board sensors and keep generating large volumes of data. Along with the recent advances in a wide range of Machine Learning (ML) algorithms, the vehicular data are being analyzed intelligently to enable users to be better informed and make safer, more coordinated, and smarter use of transport networks. The success of ML model relies on the availability of large set of relevant data so that the underlying model can be trained better. However, it is not possible for a ML model to fetch the complete set of data from a single vehicle, thus, the collaboration of other vehicles are desired in sharing their local model and collaboratively training the model. Collaborative machine learning (CML) mechanism can improve the intelligence of the ML models in different vehicles by transferring the learned knowledge from the local ML model of one vehicle to another across the distributed network.

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.