The automotive industry is going through a major transformation, being
on a path to recovery from the slowdown in the pandemic years and with the
advancements in the connected technology space. The automobile industry has
been guided by the acronym CASE (connected, autonomous, shared, electric) since early
2018. There is a recognized need for an automotive cloud that will help reduce
the costs of massive compute and data storage requirements, and will bring in
new ways of working, tools, and technologies that are enabled by cloud. The
demands have led to major shift in customer preference and expectations from
OEMs.
Three key imperatives that support CASE and will define the industry
future are:
1.
Development
of sustainable products and driving new revenues through a push towards
electric vehicles. According to the International Energy Agency (IEA), the
global EV stock will reach almost 70 million vehicles in 2025 and 230 million
vehicles in 2030 (excluding two/three-wheelers). EV stock share in 2030 will
reach 12%.[1]
2.
Enhanced
car sales through connected infotainment providing commerce and
personalization. In larger vehicles and fleet industries, there is a higher
focus on driver safety and evaluation of the driver’s physical condition which
needs camera data as well as vehicle sensor data.
3.
Thirdly,
in order to maintain sustainable operating supply chains there needs to be
intelligent manufacturing and connected supply chains running to optimum efficiency
with tracking via RFID and IoT devices. In Aug 2021,[2]
Gartner estimated that the enterprise and automotive IoT platform market will
represent a USD 11.3 billion opportunity in 2025, representing a 33% CAGR from
2020.
All of these
imperatives require dealing with large amounts of data that needs to be
processed quickly at the source – thus leading to edge and fog computing
solutions - in addition
to transporting this data for processing back to the cloud. Thus a lot of OEMs
are looking to optimize their cloud strategy and make it the foundation of
their R&D, manufacturing, sales, and servicing models.
With the
advancements in virtual reality (VR) and augmented reality (AR), there is a
play for Automotive OEMs to build experiences in the metaverse. It’s only a
matter of time before dealers jump on board and develop a metaverse dealership
in which users can view inventory, check vehicles, and complete the sales
transaction all while wearing a VR headset in the comfort of their office or
home. Gartner expects dedicated AR clouds to be formed for each sector to help synergize
the content with the underlying cloud infrastructure and remove the
requirements of local hardware.
There are many use cases for edge computing within the vehicle. The
capability to take data from the nearly hundreds of sensors, compute and take
real-time decisions from the data available onboard instantaneously, especially
around auto braking, and proximity detection to other vehicles and pedestrians
have already been embedded within automobiles. The adoption is already increasing
and has potential for many future use cases focused not only on the driver but
also the passengers, for example, real time personalized offers when the car is
moving past one’s favorite store or restaurant.
Connectivity to media streaming apps will allow personalization of the
offer by combining the data available within the vehicle OEM as well as the
streaming app tie-up. Adding to this the map data from and monitoring for
traffic congestion will allow the autonomous vehicle to reroute its path. Edge
will also allow better security monitoring and infrastructure to reduce theft
and allow for better insurance policy determination.
The typical building blocks of an automobile cloud platform include:
· Secure communication for vehicle-to-cloud connectivity
· Data ingestion for a variety of structured and
unstructured data sources – this will be process as well as transactional data
· Data migration and cleansing through cloud-native
techniques which can scale as per the volume
· Fast and scalable analytics with dashboard visualizations
to detect anomalies, and threat modelling including cabin simulations of
various scenarios
· Data models and AI to trigger workflows and
notifications such as equipment maintenance, driver alerts, and route
notifications and provide location-based services
· Secure data storage with encrypted data at rest as
well as in transit
· Will need to have a zero trust architecture because of
multiple devices and entry points that assumes every device and connection can
be breached and hence needs verification and validation
· Portability between on-premises and hyperscalers –
hence suited for a hybrid cloud architecture
Overall, the use of the automotive cloud is transforming the way the automotive industry operates and will play a key role in the development of new technologies and business models in the future.
