**3. Potential benefits**

As discussed in the previous section, cloud-based automation has gained massive interest of researches around the world and sparked many initiatives such as RoboEarth, Remote Brained Robots, IoT, Industry 4.0, VCC Industrial Internet, RoboBrain and SIoV. In this section, we have discussed potential of cloud to enhance automation of vehicle (also applicable to all robotics automation systems in general) by improving performance though five potential benefits, as follows:

## **3.1. Cloud computing**

Autonomous vehicles require intensive parallel computation cycles to process sensors' data and efficient path planning in the real-world environment [5]. It is certainly not practical to deploy massive onboard computing power with each agent of autonomous vehicle. Such deployments will be cost-intensive and may have certain limitations in parallel processing. Cloud provides massively parallel on demand computation, up to the computing power of super computers [51], which was previously not possible in standalone onboard implemen‐ tations. Nowadays, a wide range of commercial sources (including Amazon's EC2 [26], Microsoft's Azure [28] and Google's Compute Engine [52]) are available for cloud computing services, with the aim to provide access to tens of thousands of processors for on-demand computing tasks [53]. Initially, web/mobile apps developers used such services; however, they have increasingly been used in technical high-performance applications. Cloud computing can be used for computationally extensive tasks, such as to find out uncertainties in models, sensing and controls, analysis of videos and images, generate rapidly growing graphs (e.g. RRT\* ) and mapping, etc. [53]. Many applications require real-time processing of computational tasks, in such applications cloud can be prone to varying network latency and quality of service (QoS), and this has been an active research area nowadays [51, 53].

## **3.2. Big data**

Big data refers to extremely large collection of datasets that cannot be handled with conven‐ tional database systems and require analysis to find out different patterns, associations, trends, etc. [54]. Autonomous vehicles require access to vast amount of data, for example, sensors' network data, maps, images, videos, weather forecasts, programs, algorithms, etc., which cannot be maintained on board and surpass the processing capabilities of conventional database systems. Cloud infrastructure offer access to unlimited on-demand elastic storage capacities over cloud servers that can store large collections of big data as well facilitate in their intensive computations [54–56]. Shared access of big datasets can also facilitate more accurate machine learning of autonomous vehicles, which can help the planners in optimal decisionmaking. It is essential to recognize that big datasets may require high-performance IaaS tools for performing intensive computations on the gigantic amount of data. These may include Amazon's EC2 [26], Microsoft's Azure [28] and Google's Compute Engine [52], as described in previous section. Active research challenges in cloud-based big data storage include defining cross platform formats, working with sparse representation for efficient processing and developing new approaches that can be more robust to dirty data [55, 56].

#### **3.3. Open-source/open-access**

In the same year 2012, the term "Industrial Internet" was introduced by General Electric, with the purpose to connect industrial equipment over network for exchanging their data [45]. In 2014, Gerla et al. investigated the vehicular cloud and deduced that it will be a core system for autonomous vehicles that will make the advancements possible [46]. In the same year, Ashutosh Saxena announced the "RoboBrain" project, with the aim to build a massive online brain for all the robots of the world from publically available internet data [47, 48]. In early 2016, HERE announced the launch of their cloud-based mapping service for autonomous vehicles, aiming to enhance automated driving features of the vehicles [49]. In February 2016, Maglaras et al. investigated the concept of Social Internet of Vehicles (SIoV), discussed its

As discussed in the previous section, cloud-based automation has gained massive interest of researches around the world and sparked many initiatives such as RoboEarth, Remote Brained Robots, IoT, Industry 4.0, VCC Industrial Internet, RoboBrain and SIoV. In this section, we have discussed potential of cloud to enhance automation of vehicle (also applicable to all robotics automation systems in general) by improving performance though five potential

Autonomous vehicles require intensive parallel computation cycles to process sensors' data and efficient path planning in the real-world environment [5]. It is certainly not practical to deploy massive onboard computing power with each agent of autonomous vehicle. Such deployments will be cost-intensive and may have certain limitations in parallel processing. Cloud provides massively parallel on demand computation, up to the computing power of super computers [51], which was previously not possible in standalone onboard implemen‐ tations. Nowadays, a wide range of commercial sources (including Amazon's EC2 [26], Microsoft's Azure [28] and Google's Compute Engine [52]) are available for cloud computing services, with the aim to provide access to tens of thousands of processors for on-demand computing tasks [53]. Initially, web/mobile apps developers used such services; however, they have increasingly been used in technical high-performance applications. Cloud computing can be used for computationally extensive tasks, such as to find out uncertainties in models, sensing and controls, analysis of videos and images, generate rapidly growing graphs (e.g.

) and mapping, etc. [53]. Many applications require real-time processing of computational tasks, in such applications cloud can be prone to varying network latency and quality of service

Big data refers to extremely large collection of datasets that cannot be handled with conven‐ tional database systems and require analysis to find out different patterns, associations, trends,

(QoS), and this has been an active research area nowadays [51, 53].

different design principles, potential applications and research issues [50].

**3. Potential benefits**

10 Autonomous Vehicle

benefits, as follows:

RRT\*

**3.2. Big data**

**3.1. Cloud computing**

Open-source refers to free access to original source code of software and models in case of hardware, which can be modified and redistributed without any discrimination [56]. Openaccess refers to free access of the algorithms, publications, libraries, designs, models, maps, datasets, standards and competitions, etc. [57]. In open set-up, different organizations and researchers contribute and share such resources to facilitate their development, adoption and distribution. For standalone autonomous vehicles, it is not possible to maintain such opensource software and resources and take maximum advantages of the facilities. Cloud infra‐ structure facilitates by providing well-organized access to such pools of resources [6]. A prominent example of the success of the open resources in the scientific community is the Robot Operating System (ROS), which provides access to the robotics tools and libraries to facilitate the development of the robotics applications [58]. Furthermore, many simulation tools and libraries (e.g. GraspIt, Bullet, Gazebo, OpenRAVE, etc.) are available open-source and can be customized as per the application's requirement, which can certainly speed up the research and development activities.

## **3.4. System learning**

System learning refers to collective learning of all the agents (e.g. autonomous vehicles) in the system. Autonomous vehicles need to learn from each other's experiences, for example, if a vehicle identifies a new situation that was not part of the initial system, then the learning outcome of that instance needs to be reflected in all the vehicles in the systems. Accomplishing such goals is not possible with standalone implementation of the autonomous vehicles [5]. Cloud infrastructure enables shared access on the data [6]. Instances of physical trials and new experiences are also stored in that shared pool for collective leaning of all the vehicles. Instances can hold initial and anticipated conditions, boundary conditions and outcome of the execution. A good example of collective learning is the "Lightning" framework, which indexes paths of different robots in the system over several tasks and then use cloud computing for path planning and variations in different new situations [59].
