Page 35 - Cyber Defense eMagazine January 2024

Two critical enablers for autonomous driving are Edge and AI: empowering vehicles to process IoT
sensors’ data within the vehicle itself and by doing so, enabling real-time operations. This capability is
crucial for any mission-critical applications. Attempting to manually program the machine to handle every
possible driving scenario becomes an impractical endeavor. Instead, the vehicle must dynamically learn
from its environment. The intelligence of an AV hinges on the availability of various IoT sensor data,
allowing the creation of a digital representation (a twin) of the physical world. The more diverse the data,
the more sophisticated AI systems can be deployed.

When observing the evolution path of autonomous driving, we can notice a gradual reduction in human
involvement at each stage. The AV framework includes 6 levels of automation ranging from 0 (fully
manual) to 5 (fully autonomous).

• No automation: the driver retains complete control of all driving tasks.
• Driver assistance: the vehicle incorporates a single automated system that allows the driver to
take their foot off the pedal.
• Partial automation: the vehicle becomes capable of handling steering and acceleration, allowing
the driver to take their hands off the wheel.
• Conditional automation: the vehicle can control most driving tasks, enabling the driver to take their
eyes off the road while still maintaining supervision.
• High automation: the vehicle performs all driving tasks under specific conditions, giving the driver
the opportunity to take their mind off the road while remaining alert.
• Full automation: the vehicle can independently handle all driving tasks under any conditions. This
transforms the driver into a passenger, completely freeing their mind from all driving
responsibilities.

The benefits of AI in SW Development largely mirror those seen in autonomous driving:

Minimizing human errors and freeing-up time for more creativity-intensive work. Since human resources
are often the costliest aspect of SW development, organizations are incentivized to adopt AI-based
systems that can enable them to do more with less.

Closer examination of the SW development evolution paths reveals striking similarities to the
advancements in autonomous driving: gradual reduction in human involvement at each stage of
evolution:

• In the early 2000s, SW Development had little to no automation. Human control was required at
every stage of the SW Development Lifecycle (SDLC), making the process largely manual. Issues
were often identified by customers rather than internal teams.

• Fast forward to the mid-2010s, we witnessed the rise of Containerization, Cloud Computing,
and DevOps, leading to increased automation and efficiency throughout the SDLC. Routine tasks
and procedural decisions were automated based on predefined (hard-coded) policies and "if-then"

Cyber Defense eMagazine – January 2024 Edition 35
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