By Lance Eliot, the AI Trends Insider
I should sue! That’s what my friends told me to do.
They were looking at the damage done to the right fender and front right tire of my car. I had been driving innocently down a street in downtown Los Angeles and encountered a whopper of a pothole. I was doing the legal speed limit and was not driving recklessly.
When I turned a corner, an unexpected pothole loomed just after making the turn, and the right side of my car was doomed to enter into the gaping asphalt gash.
My guess is that I was going relatively slowly as I made the turn and it seemed to me that such a low speed would have made hitting the pothole a non-issue.
Nonetheless, there was a loud bang and scraping sound when I hit the pothole. I quickly pulled over to the side of the road to do a quick visual inspection of my car. The fender was scrapped and slightly bent. The tire appeared to be intact but had a strange bulbous-like protrusion now at the surface of the rubber.
Certainly, I was not the first person to find myself having become a victim of this particular pothole.
This specific street was popular since it led you to the on-ramp for the Harbor Freeway. Typically, each day, downtown L.A. office workers would go this way in the evening after work to get onto the freeway and make their way home. I’d bet that tons of drivers had hit that pothole. Maybe I should launch a class action lawsuit rather than suing solely on my own behalf!
Potholes On The Mind
Potholes like this monster tend to get worse over time. More and more cars fall into it or ram it or roll over it, all of which causes the hole to widen and deepen.
Now I’m not saying that this was one of those huge abyss-like monstrosities that seem to swallow-up entire cars. Admittedly, this pothole was still in its infancy. But, however you want to characterize it, the hole was relatively lethal and had already taken a chomp out of my car.
It was quite unsettling when I hit the pothole.
It made me wonder afterwards if other drivers might lose control of their car or become so startled that they might drive wildly after having struck the pothole. I watched for a few moments to see what other drivers did, and by-and-large most of them seemed to take the banging and bumping of hitting the pothole in stride. They probably had experienced this pothole before, or perhaps have encountered so many potholes throughout the Southern California area that they had become numb to hitting them.
There were a few drivers that struck the pothole and definitely appeared to momentarily nearly lose control of their car. They swerved toward the curb that was just a few feet from the hole. I suppose it was possible that if a pedestrian happened to be standing at that exact spot on the sidewalk, perhaps right at the curb, maybe waiting to get a ridesharing lift, they could have been endangered.
It would have to be some driver that really got a shock from plastering into the pothole, though this is not as remote a chance as you might think. There are lots of Los Angeles drivers that I’d dare say should probably not have a driver’s license as they seem to drive without any due care or drive like a frightened mouse that goes a kilter at the slightest afront. A novice teenage driver just learning to drive might be taken aback by hitting the hole and perhaps lose control of their car. Besides cars, I also considered the impact of a motorcyclist hitting the pothole and the idea made me shudder.
Some of you might be thinking that I was not properly paying attention to the road and that if I had been more alert that I would have seen the pothole before striking it. I would like to argue that point with you. I went back to the corner and drove the turn again, wanting to see if it was feasible to see the pothole before making the turn. I suppose that I was trying to amass evidence for suing, or at least to be able to explain to my friends why I “stupidly” struck a gap pothole.
At the turn, there were too many other objects nearby to be able to clearly see the roadway beyond the corner. There was a fire hydrant near the corner. There was a pedestrian stand. There was a posted sign about when you could park on that street. There was a street sign indicating the name of the street. All in all, even if you knew to look for the pothole, it was well obscured by the other objects at the corner.
Upon making the turn, you would only have a split second to see the pothole. I estimated that you would need to be crawling at the lowest possible speed of a car to have any amount of time to first notice the pothole and then take an evasive maneuver. Let’s also keep in mind that if you did magically see the pothole in time to make an evasive maneuver, what maneuver would you make?
If you tried to swing wide to the left around the pothole, there was a danger of striking another car coming down the street. If going left was unwise, going to the right was equally unwise or worse. There was insufficient room to try to go to the right of the pothole, which you’d end-up having to drive up onto the curb. You could try to weave directly over the pothole, putting your right tire just to the right of the pothole, making sure to keep the tire in the gutter next to the curb and not go up over the curb. This was a rather finesse-like approach and would have taken some advanced preparations to get to just the correct position moments before coming upon the hole.
Generally, I would say that the “safest” approach was to go ahead and bite-the-bullet and hit the pothole, assuming that you were not forewarned about its presence. Hitting the pothole and making sure to keep control of your car seemed a less risky approach than the other alternatives. Trying to hit the brakes just as you encountered the pothole was another possibility, but I’d bet that a car behind you that was also making the same turn would have been likely to rear-end your car. I realize you might say that would be their fault, and I get that notion, regardless though I’d rather take the chance of harming my suspension or my tire versus getting struck by another car from behind and possibly suffering whiplash.
For those of you in the hyper-digital age, you might be yelling at me right now and clamoring that I ought to be using a traffic app on my smartphone that might have warned me about the pothole.
Indeed, there are a number of traffic or roadway related apps that allow a crowdsourcing approach to keeping track of the deteriorating roadway infrastructure. People using the app can mark spots that contain potholes and other roadway difficulties. Other people using the app can then be forewarned.
There are some apps that also allow the posted item to be transmitted to the local roadway repair crews.
Of course, it isn’t as though a roadway repair crew is going to instantaneously appear and fix the hole or other roadway matter. The odds are that they have hundreds or maybe thousands of these kinds of reported roadway issues. They need to prioritize which ones they work on. It takes time for the crew to come out and make the repair. Presumably, the worst of the roadway blemishes that present the highest risk to drivers and pedestrians are getting the higher priority over the other bothersome but not “killer” kinds of roadway problems.
One concern expressed by those that study our crumbling roadway infrastructure is that we seem to be mired in a continual mode of quick repair. This fix-and-forget kind of approach is belied by the fact that often times a repair is made that lasts only a short time. For the pothole at the corner, suppose a repair crew slops in some fresh asphalt. It might fill-in the hole for a brief period of time. Meanwhile, cars continue to roll over the patch and the pothole will be potentially reborn. Sure enough, the pothole might become a monster again, and the cycle of coming to do another quick-fix will repeat itself.
The American Society of Civil Engineers recently published a report that says there are around 57% of the roads in Los Angeles that can be rated at a poor condition. By poor condition, they are asserting that those roads are in significant deterioration, are well-below roadway standards, and have a strong risk of overall failure. In my daily one to two-hour commute here in Southern California, I’d wholeheartedly agree that at least seemingly half of the roads are in bad shape here. Maybe more. Maybe a lot more.
Those of you that aren’t here in California are probably not especially sympathetic to our roadway plight in that you likely have something going on where you live that has a similar gloomy roadway dilemma, perhaps even worse than our roads. Here’s a big number for you: $4.6 trillion dollars. That’s how much the American Society of Civil Engineers estimates is the cumulative price needed to make our U.S. transportation infrastructure into something of an above average grade (right now, they say that the U.S. is maybe a D+).
My story about the pothole is really a microcosm of our overall roadway infrastructure. We have lots and lots of infrastructure that is crumbling around us. We depend upon the infrastructure to make our way to work and for going to the store and for living our lives. The infrastructure is decaying and wearing out. Attempts at quick fixes are only momentarily keeping things intact. One might claim that those quick fixes end-up masking the overarching problems and we are therefore deluding ourselves by making the quick fixes.
Our economy depends upon our ability to drive on the roads. One could say that our society depends on our ability to drive on the roads. Our elaborate and crisscrossing roadway infrastructure is the essence of how we live.
It is easy to take it for granted.
When I tell people that we need to do something about our roads, I usually get a kind of yawn and am told that we just need to all stop whining (though, once they themselves have hit a pothole, and felt the “pain” of our worsening roads, they suddenly become converts to doing something about the infrastructure!). When I tell people about the nearly $5 trillion dollars needed to invest in our infrastructure to keep it going and hopefully bolster it, the number is so astronomical that most people cannot fathom how much money that is.
Autonomous Cars And Foul Infrastructure
What does this have to do with AI self-driving driverless autonomous cars?
At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars. One aspect involves making sure that the AI can handle driving on rough roads and contend with our deteriorating roadway infrastructure.
Allow me to elaborate.
I’d like to first clarify and introduce the notion that there are varying levels of AI self-driving cars. The topmost level is considered Level 5. A Level 5 self-driving car is one that is being driven by the AI and there is no human driver involved. For the design of Level 5 self-driving cars, the automakers are even removing the gas pedal, brake pedal, and steering wheel, since those are contraptions used by human drivers. The Level 5 self-driving car is not being driven by a human and nor is there an expectation that a human driver will be present in the self-driving car. It’s all on the shoulders of the AI to drive the car.
For self-driving cars less than a Level 5 and Level 4, there must be a human driver present in the car. The human driver is currently considered the responsible party for the acts of the car. The AI and the human driver are co-sharing the driving task. In spite of this co-sharing, the human is supposed to remain fully immersed into the driving task and be ready at all times to perform the driving task. I’ve repeatedly warned about the dangers of this co-sharing arrangement and predicted it will produce many untoward results.
For my overall framework about AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/
For the levels of self-driving cars, see my article: https://aitrends.com/selfdrivingcars/richter-scale-levels-self-driving-cars/
For why AI Level 5 self-driving cars are like a moonshot, see my article: https://aitrends.com/selfdrivingcars/self-driving-car-mother-ai-projects-moonshot/
For the dangers of co-sharing the driving task, see my article: https://aitrends.com/selfdrivingcars/human-back-up-drivers-for-ai-self-driving-cars/
Let’s focus herein on the true Level 5 self-driving car. Much of the comments apply to the less than Level 5 self-driving cars too, but the fully autonomous AI self-driving car will receive the most attention in this discussion.
Here’s the usual steps involved in the AI driving task:
- Sensor data collection and interpretation
- Sensor fusion
- Virtual world model updating
- AI action planning
- Car controls command issuance
Another key aspect of AI self-driving cars is that they will be driving on our roadways in the midst of human driven cars too. There are some pundits of AI self-driving cars that continually refer to a Utopian world in which there are only AI self-driving cars on the public roads. Currently there are about 250+ million conventional cars in the United States alone, and those cars are not going to magically disappear or become true Level 5 AI self-driving cars overnight.
Indeed, the use of human driven cars will last for many years, likely many decades, and the advent of AI self-driving cars will occur while there are still human driven cars on the roads. This is a crucial point since this means that the AI of self-driving cars needs to be able to contend with not just other AI self-driving cars, but also contend with human driven cars. It is easy to envision a simplistic and rather unrealistic world in which all AI self-driving cars are politely interacting with each other and being civil about roadway interactions. That’s not what is going to be happening for the foreseeable future. AI self-driving cars and human driven cars will need to be able to cope with each other.
For my article about the grand convergence that has led us to this moment in time, see: https://aitrends.com/selfdrivingcars/grand-convergence-explains-rise-self-driving-cars/
See my article about the ethical dilemmas facing AI self-driving cars: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/
For potential regulations about AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/assessing-federal-regulations-self-driving-cars-house-bill-passed/
For my predictions about AI self-driving cars for the 2020s, 2030s, and 2040s, see my article: https://aitrends.com/selfdrivingcars/gen-z-and-the-fate-of-ai-self-driving-cars/
Returning to the topic of our crumbling transportation infrastructure, let’s consider what kind of impact this situation might have on AI self-driving cars, along with pondering the future of the infrastructure as framed in light of the hopeful and likely advent of AI self-driving cars.
Crumbling Infrastructure
We’ll start with the assumption that the roads will continue for the foreseeable future to deteriorate and it will be a sad and unavoidable fact of life.
As such, what should AI developers be doing in terms of the AI for self-driving cars? Some AI developers tell me that there’s nothing special they need to do. The road is the road. Good or bad, there’s presumably no need to care. Just focus on having the AI be able to drive a car and that’s sufficient, in their book.
I tend to disagree with their head-in-the-sand approach.
We believe that the AI ought to be specially prepared for a likely lousy infrastructure that contains roadway potholes, pits, cracks, debris, and for which the painted lines on the roads will be faded or disappear, and that street signs might be obscured or missing, etc. These are all the potential and inevitable consequences if there is not something Herculean done to improve the infrastructure.
One aspect that catches the attention of the AI developers that don’t seem to believe in caring about the untoward infrastructure involves my mentioning the faded or disappearance of lane markers and lane lines. This gets those AI developers to suddenly pay attention. The reason for their attention is that many of them are using the now-classic navigation technique of watching for lane markers and lane lines to know where the AI is supposed to position the self-driving car.
The AI system uses the camera sensors to try and detect where those lane markers and lane lines are. Then, once so detected, the AI guides the controls of the self-driving car to stay within those lines when traveling in a lane, and also for purposes of changing lanes. It is essential that this AI approach must have available relatively obvious and clear-cut lane markings. Without the lane markings, the AI system is pretty much unable to discern where a lane is and where to keep the self-driving car while moving along on the roads.
Human drivers of course also depend upon the lane markers and lane lines, but they are also able to handle a great deal of ambiguity when the lane indications are slim or intermittent. Us humans seem to be able to mentally gauge where a lane might or must be, even when the lane itself does not stand out or otherwise has evaporated in terms of a marked path. That’s how good us humans are. Sure, I realize that some humans do get confused in such cases, and they might weave or wander into someone else’s pretend lane, but by-and-large most capable human drivers can handle this vagueness when it occurs.
So, the point is that the traditional AI technique of relying on apparent lane markings and lane lines is likely to get undermined as the roadways worsen. It is crucial to bump-up the AI to be more sophisticated in ascertaining lane positioning. If we don’t boost the AI for this, the vaunted hope of having less fatalities due to the advent of AI self-driving cars will be called more so into question.
For my article about the zero fatalities topic, see: https://aitrends.com/selfdrivingcars/self-driving-cars-zero-fatalities-zero-chance/
For the dangers of the simpleton pied piper AI approaches, see my article: https://aitrends.com/selfdrivingcars/pied-piper-approach-car-following-self-driving-cars/
It is important that the AI be versed in defensive driving, see my article: https://aitrends.com/selfdrivingcars/art-defensive-driving-key-self-driving-car-success/
Here’s the kinds of foibles that can occur when driving, per my article: https://aitrends.com/selfdrivingcars/ten-human-driving-foibles-self-driving-car-deep-learning-counter-tactics/
Another aspect about the AI system is that it needs to be using all of its capabilities to try and detect roadway issues and obstacles, which will be even more so crucial as the crumbling infrastructure continues to degrade.
Example About Potholes
Let’s use my pothole example.
As mentioned, I was unable to detect the pothole prior to making the right turn at the corner of the downtown street. Could the AI have done a better job?
I’m not so sure it could have in this circumstance since the visual images coming into the cameras of the self-driving car would not have readily revealed the pothole beforehand, the sensors too would have been visually blocked as were my eyes by the various obstacles at the street corner, such as the light post, fire hydrant, and so on. The radar of the self-driving car would not likely have gotten a good bounce off the street area around the corner. The LIDAR would have likewise likely not been able to detect the pothole. Etc.
Once the AI started to maneuver the self-driving car around the corner, it would then have a chance at detecting the pothole. Suppose the AI was not trained to do so or otherwise was not particularly setup to cope with potholes? In that case, the odds are that the AI would drive straight into the pothole and not even realize what was happening. All of a sudden, the self-driving car would be bumping and shoved to the side, all of which might be a complete mystery to the AI. The AI might even lose control of the self-driving car per se, allowing the self-driving car to drift over into someone else’s lane or up onto the curb.
The AI might via the IMU (Inertial Measurement Unit) be able to realize that something is afoot when the overall balance of the self-driving was askew, but if it had not already detected the pothole it would be an unknown as to why the self-driving car has suddenly gone somewhat astray.
For aspects about the IMU on a self-driving car, see my article: https://aitrends.com/selfdrivingcars/proprioceptive-inertial-measurement-units-imu-self-driving-cars/
For my article about edge cases, see: https://aitrends.com/selfdrivingcars/edge-problems-core-true-self-driving-cars-achieving-last-mile/
For safety and AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/safety-and-ai-self-driving-cars-world-safety-summit-on-autonomous-tech/
For reaction time aspects of AI systems, see my article: https://aitrends.com/selfdrivingcars/cognitive-timing-for-ai-self-driving-cars/
Would the AI be able to quickly enough counter the physics of the lurch caused by the hitting of the pothole?
Would it be able to correct for the shove that the self-driving car got by rolling into and over the pothole?
Even if it was able to detect the pothole in-advance of hitting it, would the AI be able to appropriately identify the alternatives such as swerving over or trying to come to a halt and assess the risks associated with those alternatives, thus making a “reasoned” selection of what to do?
These are serious questions regarding the driving capability of the AI.
AI Development Mindset
I suppose some AI developers would assert that the AI has to be ready for potholes all the time anyway, and there isn’t a special case involved in dealing with these roadway evils. Though this is partially true, it also belies the idea that with a crumbling infrastructure the pothole is going to no longer be a rare event of an edge case nature and will instead be a probable and frequent encounter.
The AI might need to cope with having to drive down any given street and be dodging a large crack in the street there, and a pothole over here, and then another pothole a few feet to the left, and maybe debris chopped out of a pothole by a prior car that hit the hole.
I tend to refer to this as the AI dodgeball mode. The AI needs to be able to play a kind of dodgeball game of maneuvering in and around the various obstacles and roadway problems. I doubt that most AI developers have considered ensuring that the AI can handle this somewhat repeated and continual effort of lots of dodges to be strung together, doing so while keeping the self-driving car safely on the road and not hit any other cars or nearby pedestrians.
In essence, the usual assumption is that the self-driving car will encounter one anomaly, the AI will be able to deal with it distinctly, and then if another anomaly appears it will be completely later in time, considered a separate occurrence and fully independent of the first encounter. The reality is that a lot of the roads are likely to be a morass of deterioration on a given road, often due to the heavy traffic on that particular road.
Also, as a road starts to deteriorate, it often accelerates in deterioration as there is a kind of momentum that a rough road gets rougher faster and sooner than might a road that otherwise is more resilient and not prone to getting beat-up. The rule-of-thumb is that a worsening road will tend toward getting worse, perhaps exponentially so. Worseness begets more worseness. Meanwhile, a road that is in good shape will likely be at first “resistant” to getting torn-up, and only once a threshold has been reached will it become the proverbial snowball that grows to become an avalanche of snow over time.
For detecting and avoiding roadway debris, see my article: https://aitrends.com/selfdrivingcars/roadway-debris-cognition-self-driving-cars/
For my article on street scene free space, see: https://aitrends.com/selfdrivingcars/street-scene-free-space-detection-self-driving-cars-road-ahead/
For idealism about AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/idealism-and-ai-self-driving-cars/
For the dangers of AI developer egocentric mindsets, see my article: https://aitrends.com/selfdrivingcars/egocentric-design-and-ai-self-driving-cars/
One aspect that might help AI self-driving cars to contend with banged-up roads is the use of V2V (vehicle-to-vehicle) electronic communications.
When I drove around the corner and hit the pothole, it would have been handy if I could have immediately communicated with the car behind me. I might have told the driver in the car behind me to watch out for the pothole. I might have also indicated that I was going to come to a sudden halt to avoid smashing into the hole, and thus I wanted them to not rear-end my car when I came to an unexpected halt.
With the use of V2V, one AI self-driving car could indeed tell another AI self-driving car to do those kinds of things. Presumably, in an orderly fashion, one AI helps another AI. Each self-driving car that follows the next would be forewarned about the pothole. This would also allow those AI self-driving cars to act in concert with each other, often referred to as a swarm, allowing each to avoid the pothole by making timed and coordinated maneuvers.
For more about OTA, see my article: https://aitrends.com/selfdrivingcars/air-ota-updating-ai-self-driving-cars/
For swarm and AI, see my article: https://aitrends.com/selfdrivingcars/swarm-intelligence-ai-self-driving-cars-stigmergy-boids/
V2V Is Not The Silver Bullet
The one rub to this V2V is going to be the human drivers that are in the mix of the cars on the roads.
Suppose that when I took the corner that I was in a Level 5 self-driving car and it tried to use V2V to warn the car behind me. It could be that the car behind me was also a Level 5 self-driving car and it had V2V and it electronically listened to my self-driving car and abided by the suggested driving aspects. Or, it could be that a human driver was in the car behind me. Would they even receive the V2V? If they did receive the V2V would they opt to abide by the suggestions made by my AI of my self-driving car?
There is also the likely advent of V2I (vehicle-to-infrastructure) electronic communication. Suppose that there is a computing device somewhere near to the corner that I was turning at (these devices are sometimes referred to as edge computing devices). The device might have already gotten an indication that there is a pothole there at the corner and that it is scheduled to be repaired in a month’s time. Meanwhile, it beacons out a message that there is a pothole and be wary of it. An AI self-driving car outfitted with the V2I would receive the message and be alerted to deal with the matter.
Part of the reason that the roadway infrastructure might hasten to deteriorate could partially be due to the advent of AI self-driving cars.
You might be shocked to think that the AI self-driving car emergence could somehow worsen the roadway infrastructure, since the AI is supposed to be a polite driver that obeys the laws and tries to drive as cleanly and legally as possible (I’ve debunked those assumptions, by the way!).
The reason that the advent of AI self-driving cars will likely exasperate the crumbling infrastructure is due to the belief that we’ll want to use the AI self-driving cars non-stop. It is anticipated that AI self-driving cars will be used extensively for ridesharing purposes. You are at work during the day and allow your AI self-driving car to be making money for you while you are at the office. Likewise, at night time, while your head is nestled on your pillow in bed, your AI self-driving is out there making money.
The odds are that we are going to see a beehive of activity of self-driving cars cruising around night and day, waiting to pick-up and drop-off passengers. This continual driving is going to put more miles onto our already destitute roadways. More miles on falling apart roads means those roads will continue to fall apart. We can predict it will make those roads a lot worse. The constant pounding of self-driving car after self-driving car is a punishment that a crumbling infrastructure will not be able to readily withstand.
For non-stop use of AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/non-stop-ai-self-driving-cars-truths-and-consequences/
For the affordability aspects of AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/affordability-of-ai-self-driving-cars/
For my article about why AI self-driving cars will drive illegally, see: https://aitrends.com/selfdrivingcars/illegal-driving-self-driving-cars/
For edge computing, see my article: https://aitrends.com/selfdrivingcars/edge-computing-ai-self-driving-cars/
I suppose one potential good news is that the AI self-driving cars will hopefully make use of Machine Learning (ML) and be able to therefore increasingly get better at detecting lousy roads and sufficient driving on lousy roads. I had mentioned earlier the V2V of AI self-driving cars sharing with each other. Another form of sharing will be via OTA (Over-The-Air) electronic communication.
OTA consists of the AI self-driving car providing to the cloud of the automaker or tech firm the data that the AI self-driving car is collecting while driving on the roads. This would include the camera data, video data, radar data, and so on. At the cloud level, the auto maker or tech firm can do analyses and try to use ML and deep learning to improve how the AI self-driving cars operate. These improvements can be pushed back down into the AI self-driving car, providing updates or patches for when something amiss in the software needs to be upgraded or fixed.
Let’s consider again my pothole example again. An AI self-driving car might already have been forewarned about the pothole because a prior AI self-driving car in the same fleet had reported it to the cloud via OTA. The aspect about this pothole was then brought back down into the rest of the AI self-driving cars in the fleet via the OTA too. Furthermore, beyond just having a mapped indication of where the pothole is, the Machine Learning aspects would have tried to figure out ways to contend with the pothole.
Thus, the AI self-driving cars in the fleet would not only be aware of the existence of the pothole, but also have some driving tactics and strategies to contend with it. Perhaps one aspect would be to not even make that right turn and go up another street to make the desired right turn. Another tactic might be to swing wide when making the turn, doing so by first warning the car next to the AI self-driving car. Each of these tactics would be contextually based, meaning that the choice is not always the same one, and instead that the context such as the time of day or the weather conditions might dictate which choice is the best at the moment of making the driving decision.
For my article about imitation as a deep learning technique, see: https://aitrends.com/selfdrivingcars/imitation-deep-learning-technique-self-driving-cars/
For Ensemble Machine Learning, see my article: https://aitrends.com/ai-insider/ensemble-machine-learning-for-ai-self-driving-cars/
For Federated Machine Learning, see my article: https://aitrends.com/selfdrivingcars/federated-machine-learning-for-ai-self-driving-cars/
For my article about biomimicry, see: https://aitrends.com/selfdrivingcars/biomimicry-robomimicry-ai-self-driving-cars-machine-learning-nature/
I’ve focused so far on having the AI be adept at contending with a crumbling infrastructure.
Perhaps I should not be so fatalistic.
Let’s imagine that we collectively have the willpower to do something substantive about the crumbling infrastructure.
Doing Something About The Infrastructure
Depending upon the status of AI self-driving cars at the juncture of moving forward on improving the infrastructure, we could use the data from the AI self-driving cars to better understand where the crumbling infrastructure is most occurring. Keep in mind that the AI self-driving cars will have their myriad of sensors and will be crisscrossing the roads and continually capturing visual images, radar, LIDAR, etc.
This is a huge amount of data that can be used to mine when trying to prioritize where to put our energies and money on infrastructure improvements. This data can reveal which roads are most traveled and which are least traveled. It can reveal the roughness of the roads. There are a slew of handy analyses and metrics that can be discerned from this vast collection of data.
Another factor involves whether or not to merely fix the infrastructure as though we will continue to have only conventional cars, or whether to consider doing other kinds of improvements or upgrades to the infrastructure that tie into the advent of AI self-driving cars.
For example, I had mentioned herein the use of edge computing, which will be a boon to AI self-driving cars. Perhaps the crumbling infrastructure can be enhanced by the adoption of edge computing.
There is also going to be the OTA taking place and we need fast networks to handle that kind of data movement. I’ve already previously described the importance of 5G in my speeches and writings. Perhaps the infrastructure can include the adoption of 5G on a widespread basis across our roadways.
We will need a provision for dealing with AI self-driving cars that breakdown. I realize that some pundits claim that AI self-driving cars will never breakdown, but this is crazy talk. A car is a car. There will be lots of reasons for an AI self-driving car to breakdown, including as previously pointed out that they will be trying to run non-stop 24×7. We’ll need to contend with the towing of broken-down AI self-driving cars, another topic that I’ve covered in my presentations and writings, and for which the infrastructure can be shaped to aid toward appropriately handling these situations.
For my article about 5G, see: https://aitrends.com/selfdrivingcars/5g-and-ai-self-driving-cars/
For towing an AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/towing-and-ai-self-driving-cars/
For repairing of AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/auto-recalls/
For when AI self-driving cars freeze-up, see my article: https://aitrends.com/selfdrivingcars/freezing-robot-problem-and-ai-self-driving-cars/
Conclusion
With the existing roadway infrastructure that is falling apart at the seams, we need to be ready for the advent of AI self-driving cars. It would be a shame to have AI self-driving cars that cannot readily use what might be impassable roads by the time that the AI is ready to hit the roads. Think of the irony that we might have in-hand self-driving cars, but they cannot go anywhere because of the marred roads. Or, we might put AI self-driving cars onto the roads, and their working for us non-stop causes the roads to hasten in crumbling.
One aspect involves making sure that the AI is savvy enough to be able to deal with the lousy infrastructure. There is though only so much that the AI can do in this regard. It would be like having all human drivers have to learn to drive gingerly so as to not unduly upset the roads. Better still would be to fix the infrastructure.
Fixing it means not just making what already exists passable, it also means that we would want to perform upgrades and improvements that dovetail with the emergence of AI self-driving cars. The motto often heard of “fix the darned roads” should be augmented by the clamor to “tech-up the roads” so that we’ll have a synergistic effect of good tech-savvy roads that coincide with the prevalence of AI self-driving cars.
Come to think of it, I’m going to have some signs made-up that say this and stand at the pothole tomorrow to alert my fellow mankind of what we need to do next.
Wave at me and honk your horn in support, would you please?
Copyright 2020 Dr. Lance Eliot
This content is originally posted on AI Trends.
[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/]
