Overcoming limitations of AI and machine learning in autonomous vehicles
Autonomous vehicles are dependent on historical and real-time data, without which artificial intelligence and machine learning would be impossible.
They aren’t plug-and-play because not all of the potential scenarios can possibly be predicted by the software developers, simulators or data modelers. Simulators, and to a degree connected and autonomous vehicles (CAVs) themselves, are also only as good as their algorithms and the data inputted into them.
Consequently, AI and machine learning in autonomous vehicles can be limited, so nobody should expect them to instantly be able to cope with every potential scenario. Their development has to be taken with a sense of caution to prevent unintended consequences from occurring. The limitations of CAVs aren’t just about the AI and machine learning technology. There is also a need to educate consumers about what they can and cannot do safely. Perhaps, for this reason, there will be, for quite some time, the need for a human driver to have the ability to take back control.
“Enabling AI everywhere requires device makers and developers to deliver machine learning locally on billions, and ultimately trillions of devices,” adds Dipti Vachani, senior vice-president and general manager of automotive and IoT line of business at Arm. “With these additions to our AI platform, no device is left behind as on-device ML on the tiniest devices will be the new normal, unleashing the potential of AI securely across a vast range of life-changing applications.”
Meanwhile, Danny Shapiro, senior director automotive at Nvidia claims that it’s hard to say “whether there is one location that is ahead of others” in the connected and autonomous vehicle market. However, he says there is so much development work ongoing with pilot projects running and “production vehicles are all occurring simultaneously”. He is certain that “everything that moves will eventually have some level of automated or autonomous capability.”
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