Data blurring is when a system intentionally makes parts of an image or video harder to recognize. For self-driving cars, that can help hide faces or other identifying details. The car still tries to understand the road, but with less personal information exposed.
Self-driving cars are cars that use computers to do the driving. Instead of a person controlling everything, the car’s AI handles things like speed and steering. To do that, it uses sensors to “see” what’s around it.
An AI driving system is the car’s computer brain. It looks at what the sensors detect and decides what the car should do next. In this context, it’s what actually performs the driving actions.
Self-driving cars use multiple sensors—like cameras or other detectors—to understand what’s happening around the vehicle. The episode’s point is that these sensors can collect a lot of detail. That’s why privacy becomes an issue.
“Roving eye” means the idea that self-driving cars could constantly watch the world as they travel. That raises privacy worries because it could reveal where people go.
Sensory data is the information the car gathers from its “eyes and ears,” like cameras and other sensors. The car uses it to figure out what’s happening around it.
Video cameras are imaging sensors that capture visual information (frames) used for perception and recording. In the privacy context, they can reveal identities, locations, and activities when their footage is stored, transmitted, or analyzed.
Blurring techniques are ways to hide identifying details in images, like faces or license plates. The goal is to protect privacy without ruining the car’s ability to understand the road.
The foreground is the area in front of the car that matters most for driving. If the car can clearly see the foreground, it can react to nearby hazards better.
The background is everything farther away behind the main objects the car is reacting to. Sometimes it needs to be clear for navigation and context, but sometimes it can be treated differently.
Intentional blurring means the system intentionally “messes up” parts of the sensor information to protect privacy. It tries to keep the useful driving details, while hiding things that could identify people.
The onboard system is the car’s in-vehicle computing hardware and software that receives sensor inputs and performs perception and driving-related processing. The transcript highlights that blurring “while the data is fresh” requires enough onboard compute power to do it in real time.
Here, “cloud” means data is sent to remote computers over the internet. The debate is whether the car should blur data before sending it, or send it first and blur it after it reaches those remote servers.
Unblurred data is the original, not-yet-privacy-protected information from the sensors. The debate is whether that raw data should stay in the car or be sent somewhere else after additional safeguards.
Blurring algorithms are the rules or computer methods used to hide sensitive details in the data. The tricky part is that other methods can sometimes undo that hiding, so the “hide” and “undo” tools keep improving.
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Hi, I'm Dr. Lance Elliott and welcome to my podcast series about self-driving cars.
In this episode, I'll be discussing the topic of using data blurring for self-driving cars.
If you've become interested in learning more about self-driving cars, please see my website
www.ai-self-driving-cars.guru for further information.
Okay, let's get started. Have you ever taken a snapshot of someone and realized that the person
in the picture appears blurry due to your camera being out of focus? I'm sure you've done this
before. In some cases, the blur happens entirely by accident, whereby you should have set the
focus but failed to do so. There are situations involving the use of a blur for intentional
purposes. Perhaps you have a few friends that you want to take their pictures of, and behind them
there's a mess and other people making quite a scene. You don't want the background to overtake
the attention of the foreground. Therefore, you set the focus to make the background blurry.
Now, why this discussion about blurrs and images? The use of a blurring technique can
potentially be used as a means of trying to attain a level of privacy for those that might be
captured on an image or video or altogether in data. Shifting gears, I promise to come back to
the blurrs in a moment. Let's talk about cars. The future of cars consists of self-driving cars.
These are cars that have an AI driving system at the wheel of the vehicle. The driving actions
are undertaken by AI. An important element of self-driving cars is the use of various sensors
to detect the driving scene. Though this seems relatively innocent and there isn't much attention
being given to the plethora of sensors that self-driving cars contain. No big deal, it seems.
But here's the rub. Though sensors can capture a lot more than you might at first imagine,
suppose that all of your neighbors, for example, were to put video cameras on the rooftops of
their cars. They could be recording anything that they might encounter while on a driving journey.
Welcome to the emerging world of self-driving cars. These seemingly heralded self-driving cars
are going to be capturing imagery and other data about whatever they detect, wherever they go,
all the time that they're underway. Now, I don't want you to be on the edge of your seat,
but imagine that this massive amount of data was collated and assembled to try and piece together
the daily efforts in any given city or town. In theory, you could pull together the data from
all the self-driving cars and pretty much recreate a semblance of where people were,
when they were there, and so on. I've referred to this as the roving eye of the coming era
of self-driving cars. Do you want just anyone to know where you were last Monday or Tuesday?
Some people are already worried about privacy intrusion from video cameras that are mounted
on telephone poles, or that are used by people as they carry around their smartphones. In a sense,
that's peanuts in comparison to the magnitude of video and other sensory data captured by
self-driving cars. The more we adopt and utilize self-driving cars, the greater the amount of
observing of our daily lives is going to occur. It's as simple as that. Now, you might be thinking,
if this is a looming problem, perhaps someone ought to be doing something about it.
Well, the earlier discussion about the blurring of information or data was, in fact,
the answer to this potential problem. Here's an intriguing question to ponder.
Will the advent of AI-based, true self-driving cars and the roving eye be potentially made more
societally palatable via the use of blurring techniques? Let's dig into how that might work.
There's not much debate that the foreground of any sensory detection by a self-driving car is
likely to be crucial for the driving of the vehicle. As such, the foreground is going to
have to be kept in focus. The more open-ended question is whether the background needs to
be kept in focus too. Of course, you can regularly argue that this dividing line between the foreground
and the background is altogether untenable. Suppose, for example, that a dog is running
around in someone's front yard. We probably want the self-driving car to detect that a dog is up
ahead, and though currently inside their yard, the dog might decide to dart into the street
once a self-driving car comes along. Some would assert that it makes absolutely no sense to
intentionally undercut the capabilities of the sensors in a self-driving car. Under that thinking,
we might wish to momentarily hear and set aside the notion of trying to prevent the sensors from
capturing whatever they can potentially detect. Assume that the sensors are going to be allowed
to detect as much as they can. The sensory data flows into the onboard process of the self-driving
car. The moment the sensory data has been analyzed for driving purposes, maybe then it should just
simply be deleted. Well, that presents a number of additional challenges. This is where intentional
blurring comes to play. One approach consists of taking the data after it has been examined for
driving purposes, and then blurring the data so that those elements are generally considered
irrelevant to the driving act can no longer be discerned. You don't necessarily delete those
aspects, you blur them. A difficult question arises about when the right time is to do this blurring.
If you do so while the data is fresh and just brought into the onboard system,
this means that you need the computational resources onboard the car to do this type of blurring
action. Another notion is you could do the blurring once the data has been uploaded into the
cloud. This brings up there are two camps at this. One camp says the full and unblurred data should
never be allowed to leave the car. In that sense, it should not be allowed to be uploaded to the
cloud. Only once it has been blurred and possibly encrypted could it be uploaded. The other camp
says it's find up one of the whole shebang. As long as it's encrypted, you just blur it after it
gets into the cloud. There are a lot of more bunking branches that can be thrown into this
or any matter. Let's suppose the data does get blurred. Sometimes that which can be blurred
can later on be unblurred. This means that the blurring might be undone, mathematically or
computationally. There is an ongoing cat and mouse gambit of blurring algorithms that go to
battle with each other. Someone comes up with a new or better blurring routine and someone else
then comes up with a new and improved algorithm for unblurring. At this time, few though are
generally worried about the roving eye of self-driving cars. It's just too early, they would
say. Over time, perhaps my exhortations will only become a blur, though I'm really hoping that they
become unblurred in time for appropriate thought and action to be taken about this mesmerizing
and rather clear-cut dilemma that we ultimately will face. There really is no blur about it.
Thanks for listening. Again, I'm Dr. Lance Elliott. I hope that you found today's episode
informative. If you're interested in learning more about self-driving cars, please see my
website, www.ai-selfdriving-cars.guru, for further information.
About this episode
AI self-driving cars rely on constant sensor capture, creating a “roving eye” that could reconstruct where people go and when—far beyond today’s smartphone or roadside cameras. Dr. Lance Eliot explores whether intentional data blurring can make this surveillance more acceptable without harming driving performance, keeping the foreground clear while obscuring background details. He weighs on-car vs cloud blurring, encryption and upload policies, and the risk that blurring could be reversed via evolving “blur/unblur” cat-and-mouse algorithms.
Dr. Eliot explores how the sensory data from AI self-driving cars can be intentionally blurred to help protect the privacy of those seen and heard by the roaming AI. See his Forbes column for further info: https://www.forbes.com/sites/lanceeliot/
