**1. Introduction**

The advancements in the vehicular industry have immensely benefited different related industries and served the humanity by increasing the efficiency of our routine activities. Take the example of agriculture industry, one can cultivate a piece of land so quickly (using trac‐ tors and other equipment) than compared to 100 years ago. The same applies to the transpor‐ tation industry—nowadays, one can travel from one point to another so quickly compared to

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travelling few decades ago. However, vehicular industry still requires further advancements to decrease/eliminate the human error/involvement. If we take the example of transportation industry, only in the USA, motor-vehicles-traffic-related injuries result in around 34,000 deaths every year, and it is the leading cause of death every year for people aged between 4 and 34 [1]. If we look worldwide, over 1.20 million people die every year as a result of road traffic acci‐ dentsandbetween20and50millionmorepeoplesufferfromnon-fatalinjuriesincludingphysical disabilities, etc. [2]. 90 plus percent of these accidents are caused by human error [2]. Hence, a need arises for the technology that always pays attention to it, never gets distracted and has no/ minimal human involvement. Such goals can be achieved through autonomous vehicles.

**Figure 1.** Different types of vehicles used in real life applications for travelling in different mediums such as land, wa‐ ter, air and space.

Vehicles can be generally categorized into on road, off road, water, aerial, space and amphib‐ ious vehicles. **Figure 1(a)**–**(f)** shows examples of different types of vehicles from real life. Onroad vehicles require paved or gravel surface for their driving. These vehicles involve sports cars, passenger cars, pickup trucks, school/passenger buses, etc. Off-road vehicles are vehicles which are capable of driving both on as well off the paved surface. These vehicles are generally characterized with deep large tires or caterpillar tracks, flexible suspension and open treads. Tractors, bulldozers, tanks, 4WD army trucks are examples of such type of vehicles. Both onroad and off-road vehicles require land as a medium of travelling. Water vehicles are vehicles which are capable of driving/travelling on water, under water or both on as well under water. Ships, boats and submarines are examples of such vehicles. Aerial vehicles are vehicles which are capable of flying in the air by gaining air support. Aeroplanes, helicopters, drone planes are examples of aerial vehicles. Space vehicles are vehicles which are capable of travelling/ flying in the outer space. They are used to carry payload such as humans or satellites between the earth and the outer space. These vehicles are rocket-powered vehicles, which also require an oxidizer to operate in vacuum space. Spacecrafts and rockets are examples of space vehicles. Amphibious vehicles are vehicles, which inherit characteristics of multiple mediums of travelling and can travel in those mediums efficiently. These mediums can be land, water, air and space. AeroMobil 3.0 and LARC-V (Lighter, Amphibious Resupply, Cargo, 5 ton) are examples of amphibious vehicles.

Automation of these different types of vehicles can increase the safety, reliability, robustness and efficiency of the systems through standardization of procedural operations with minimal human intervention. With the advancements in technology, autonomous vehicles have become forefront public interest and active discussion topic recently. Based on a recent survey conducted in the USA, UK and Australia, 56.8% peoples had positive opinion, 29.4% had neutral opinion and only 13.8% had negative opinion about the autonomous or self-driving vehicles [3]. These stats give us a good picture of the general public's interest in the autonomous vehicles; however, they do have high levels of concerns regarding safety, privacy and per‐ formance issues. There are generally five levels of autonomous or self-driving vehicles ranging from Level 0 to Level 4 [4]. The brief description of these levels is as follows (taken from [4]):

**•** Level 0 means no automation.

travelling few decades ago. However, vehicular industry still requires further advancements to decrease/eliminate the human error/involvement. If we take the example of transportation industry, only in the USA, motor-vehicles-traffic-related injuries result in around 34,000 deaths every year, and it is the leading cause of death every year for people aged between 4 and 34 [1]. If we look worldwide, over 1.20 million people die every year as a result of road traffic acci‐ dentsandbetween20and50millionmorepeoplesufferfromnon-fatalinjuriesincludingphysical disabilities, etc. [2]. 90 plus percent of these accidents are caused by human error [2]. Hence, a need arises for the technology that always pays attention to it, never gets distracted and has no/ minimal human involvement. Such goals can be achieved through autonomous vehicles.

**Figure 1.** Different types of vehicles used in real life applications for travelling in different mediums such as land, wa‐

ter, air and space.

2 Autonomous Vehicle


These levels of automations are drafted for the on-road vehicles; however, they can be extended for the other types of vehicles. Based on a recent survey conducted in the USA, UK and Australia, 29.9% people were very concerned, 30.5% were moderately concerned, 27.5% were slightly concerned and only 12.1% people were not at all concerned about riding in a Level-4 autonomous vehicle [3]. These statistics show that major efforts are required for the usage and acceptability of the autonomous vehicles in real-world practical applications.

Autonomous vehicle is an active area of research and possesses numerous challenging applications. The earlier implementations of the autonomous vehicles and other autonomous systems were standalone implementations; rather, most of the existing implementations are still operating independently [5]. In such standalone implementations, the system is limited to the onboard capabilities such as memory, computations, data and programs, and also the vehicles cannot interact with each other or have access to each other's information or infor‐ mation about their surroundings [5]. To achieve Level-4 autonomous vehicles and self-driven automation in other robotic systems, it is important to overcome these limitations and go beyond the onboard capabilities of such systems. With the advent of Internet and emerging advances in the cloud robotics paradigm, new approaches have been enabled where systems are not limited to the onboard capabilities and the processing is also performed remotely on the cloud to support different operations. The cloud-based implementation of different types of vehicles has been illustrated in **Figure 2**. The cloud-based infrastructure has potential to enable a wide range of applications and new paradigms in robotics and automation systems. Autonomous vehicle is one example of such systems, which can highly benefit from cloud infrastructure and can overcome the limitations posed by standalone implementations. Cloud infrastructure enables ubiquitous, convenient and on-demand network access to a shared pool of configurable computing resources [6]. These computing resources can include services, storage, servers, networks and applications [6]. These resources can be rapidly provisioned and released with minimal service provider interaction or management effort [6]. The cloud model is characterized with five important characteristics (on-demand self-services, broad

**Figure 2.** Example of vehicular cloud where vehicles act as nodes to access shared pool of computing resources, includ‐ ing services, storage, servers, networks and applications.

network access, resource pooling, rapid elasticity and measured service), three service models (Software as a Service, Platform as a Service and Infrastructure as a Service, abbreviated as SaaS, PaaS and IaaS, respectively) and four deployment models (private, community, public and hybrid clouds), and see Ref. [6] for more detail.

autonomous vehicle [3]. These statistics show that major efforts are required for the usage and

Autonomous vehicle is an active area of research and possesses numerous challenging applications. The earlier implementations of the autonomous vehicles and other autonomous systems were standalone implementations; rather, most of the existing implementations are still operating independently [5]. In such standalone implementations, the system is limited to the onboard capabilities such as memory, computations, data and programs, and also the vehicles cannot interact with each other or have access to each other's information or infor‐ mation about their surroundings [5]. To achieve Level-4 autonomous vehicles and self-driven automation in other robotic systems, it is important to overcome these limitations and go beyond the onboard capabilities of such systems. With the advent of Internet and emerging advances in the cloud robotics paradigm, new approaches have been enabled where systems are not limited to the onboard capabilities and the processing is also performed remotely on the cloud to support different operations. The cloud-based implementation of different types of vehicles has been illustrated in **Figure 2**. The cloud-based infrastructure has potential to enable a wide range of applications and new paradigms in robotics and automation systems. Autonomous vehicle is one example of such systems, which can highly benefit from cloud infrastructure and can overcome the limitations posed by standalone implementations. Cloud infrastructure enables ubiquitous, convenient and on-demand network access to a shared pool of configurable computing resources [6]. These computing resources can include services, storage, servers, networks and applications [6]. These resources can be rapidly provisioned and released with minimal service provider interaction or management effort [6]. The cloud model is characterized with five important characteristics (on-demand self-services, broad

**Figure 2.** Example of vehicular cloud where vehicles act as nodes to access shared pool of computing resources, includ‐

ing services, storage, servers, networks and applications.

acceptability of the autonomous vehicles in real-world practical applications.

4 Autonomous Vehicle

An example of cloud-based implementation is the online document-processing facility offered by Microsoft through Office 365 and OneDrive. One can perform different operations online, such as one can create, edit and share MS Office documents (Word, Excel, PowerPoint, etc.) online without the need of installing the MS Office locally. The documents' data and software installations reside on the cloud remote servers, which can be accessed through the Internet. These servers share their computing capabilities such as processors, storage and memory. Cloud-based implementations provide economics of scale and take care of the software and hardware updates. The infrastructure also facilities backup of data as well sharing of resources across different applications and users.

Cloud-enabled robots and autonomous systems are not limited to onboard capabilities and rely on data from a cloud network to support their different operations. In 2010, James Kuffner explained the potential benefit of cloud-enabled robots and coined the term "Cloud Robotics" [7]. The vehicular industry is rapidly evolving; nowadays, vehicles are equipped with different ranges of sensors and cloud collaborations, which facilitate drivers with the desired informa‐ tion, for example, weather forecasts, GPS location, traffic situation on the road, road condition, directions and time to reach the destination with different alternative paths and speeds, etc. Through cloud robotics, we can develop a network of autonomous vehicles (such as vehicular cloud or Internet of vehicles), by which autonomous vehicles can collaborate with each other or can perform different computing activities that they cannot perform locally, to achieve their well-defined utility functions (e.g. timely delivery of the passengers or payload, safety, environment friendly, etc.). Google's self-driving car demonstrates the cloud-robotics-based implementation of the autonomous vehicles. It uses cloud services for the accurate localization and manoeuvring. Google tested the autonomous vehicle deployment on different types of cars including Audi TT, Lexus RX450h, Toyota Prius and their own custom vehicle [8, 9]. As of March 2016, Google had tested their autonomous self-driven vehicles a total of 1,498,214 miles (2,411,142 km) [10]. The project is limited to the on-road implementation of the autono‐ mous vehicle; also it has many limitations that need to be addressed before it can be released to the commercial public. These limitations involve driving in heavy rain or snowy weather, driving on unmapped intersections or routes, veer unnecessarily due to difficulty in objects identification and other limitations cause of LIDAR technology to spot different signals (e.g. police officer signalling the car to stop), etc. [11, 12].

The rest of the chapter is organized as follows: In Section 2, we have presented the historical backgrounds of the evolution of autonomous vehicles, cloud computing and cloud-enabled autonomous vehicles. In this section, we have also presented different high level architectures of autonomous vehicle and cloud-enabled autonomous vehicles proposed in literature. In Section 3, we have discussed five potential benefits of cloud-enabled autonomous vehicles, namely cloud computing, big data, open-source/open-access, system learning and crowd‐ sourcing. Section 4 describes active research challenges and possible future directions in the field, and conclusion appears in Section 5.
