Coming Soon: Modular Autonomous Vehicle Systems (MAVS) and AI Self-Driving Cars

Dr. Lance B. Eliot, AI Insider

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Concept MAVS Unveiled by Mercedes-Benz

Eli Whitney is well-known for his invention of the cotton gin and for using the budding approach of interchangeable parts to produce his ingenious device. Thomas Jefferson was later to credit the dawning of the machine age to Eli Whitney.

We now know that interchangeable parts is crucial to any kind of mass production.

A close brethen of interchangeable parts is the concept of modularization.

It makes sense to structure or architect your device or system into a series of modules when you are considering using an interchangeable parts approach. By decomposing something into various components or modules, you then can devise them so that they fit together well.

In the software field, any software engineer worth their salt knows that modularity is vital to developing software. This is especially the case when the software is large-scale in size.

The development of AI systems is likewise usually benefited by structuring the system into modules. You are bound to have parts of the AI system that are leveraging AI techniques and capabilities, meanwhile there are likely other parts that are more conventional in their approach. You can architect the overall system into those modules that are AI specific and those that are more conventional.

If you did subdivide an AI system into two overarching halves or layers, consisting of a half that had the AI and the other half that had more traditional elements, it would be a type of cleaving that might be handy. Presumably, at a later date, you might discover newer AI techniques and could then focus on embodying those into the AI half, possibly being able to leave the traditional element layer untouched.

Interchangeable Parts, Modularization and Cars

Let’s consider for a moment how the concepts of interchangeable parts and of modularization are related to cars.

By-and-large most automobiles are devised of interchangeable parts.

The engine is likely able to be popped into the rest of the car and allow you to make the engines without having to do so as tailored to the particular car body and other car elements. It is all modularly devised and done so with interchangeable aspects, meaning too that you need to upfront get everything well figured out so they will fit together without difficulty.

I’m guessing that most of us realize that the modularization of cars makes a lot of sense and have become accustomed to the notion.

Remember how I earlier suggested that for an AI system we might modularize it into at least two major halves or layers, consisting of a layer that has the AI components and a second layer with the more traditional components. I’m not saying you need to do things that way, and only pointing it out as a possibility, which might or might not make sense depending upon the nature of the AI system that you are creating. Also, be clear that the modularization exists within those halves too, and thus I am not somehow suggesting that there are merely two monoliths, and instead saying that you could have a major modularization at the topmost level and then have modularization within those subsequent layers or modules.

For cars, suppose we tried to employ the same idea of dividing a car into two major halves or layers. If so, what would those layers consist of?

In the case of a car, the seemingly most logical way to cut it into half would be to devise a lower half and an upper half. We’ll refer to the lower half as the chassis of the car, which can also be referred to in many other ways, including calling it the platform, the undercarriage, the powertrain, the skateboard, and so on. The upper half we’ll refer to as the body, or the body type, or some like to say it is a pod.

Let’s then say that we’re going to design a car that has two major halves, a lower half that is the chassis or platform, and an upper half that is the body or pod.

Why would we do this?

As long as we adhere to the interchangeable parts mantra, in theory we can make the chassis or platform in one place and later on attach the body or pod in another place.

This though is merely the advantage of production. We can also gain the advantage of possibly being able to reuse the chassis or platform and have a variety of body types or pods that we can place onto the chassis. This means that we can more likely have a multitude of different cars, which are hopefully easier to produce, by having our own “standardized” chassis or platform and then making a variety of pods that would be plopped onto the platform or chassis.

Thus, rather than making two completely different cars, suppose we considered making a lower half or layer that was the mobility portion, and the upper half was the body type or pod. We could then aim to make a variety of upper halves that could plug into our “standardized” lower layer. Doing so would give us a great deal of flexibility.

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Rather than trying to make one car that can do two things, we might instead consider making two cars that each does one thing well. The problem though is that we would normally need to make two different cars, which adds up in cost to produce and makes the prediction problem harder as to how many to make.

A solution might be to go ahead and make a chassis or platform that we can use universally for being the mobility center for whatever kinds of upper layers or pods that we might want to make. We could make one pod or upper layer that can superbly accommodate passengers. We could make another pod or upper layer that is well-suited to transporting goods. Etc.

This seems like a robust way to get the best of both worlds.

Of course, the catch consists of being able to actually be able to bring together the two halves and do so in a manner that is not overly arduous to achieve. Making a plug-and-play of an entire chassis with an entire pod, it’s a challenge.

This concept of having a lower layer of the car and an upper layer is often referred to as a modular vehicle. Auto industry insiders might refer to this as the Ridek, which I’ll explain next.

A patent was awarded in the year 2000 to Dr. Gordon Dower for his invention of a modular vehicle, which also was an EV (Electrical Vehicle). The lower layer contained the engine, transmission, and so on, and he referred to it as the Modek, signifying the motorized or mobility layer (aka the chassis or platform). The upper layer was the Ridon. When you put together the Ridon with a Modek you got yourself a Ridek, the then usable car as a whole. General Motors (GM) came along in 2004 and attempted to patent a modular vehicle which they called the Autonomy. This led to a dispute about the matter.

When I attended this year’s Consumer Electronics Show (CES) in January, the modular vehicle approach was a bit of a splash since Mercedes-Benz was unveiling their Urbanetic, a concept car based on the lower layer and upper layer approach. The pod or upper layer for passengers will accommodate about a dozen occupants. There will be windows, a moonroof, and LED displays, all of which would befit the needs of human passengers. A pod for transporting goods, considered a cargo module, will have around 350 cubic feet of space and could accommodate perhaps ten pallets or so of goods.

Can A Modular Car Be Cool?

One question that some have asked about these modular designs is whether the resultant car will look attractive or not. Will people be willing and eager to ride inside an AI self-driving car that looks off-putting? Maybe yes, maybe no.

Let’s next consider some of the nitty gritty about modular autonomous vehicles.

I recently participated in the Autonomous Vehicles 2019 (AV19) Silicon Valley summit, doing so in February, and had a chance to speak with Mark Crawford, Chief Engineer at the Great Wall Motors Company. He shared with me and the attendees a glimpse at how they too are pursuing a modular vehicle approach, packed with AI and autonomous capabilities.

Those of you that follow the automotive industry are likely aware of the rise of the Great Wall Motors Company, a Chinese automobile maker. They are the biggest producer of SUV’s for China and made a noteworthy accomplishment in 2016 when they passed the one million mark of cars sold in that year.

This all brings me to the topic of MAVS.

Modular Autonomous Vehicle Systems (MAVS) is an up-and-coming buzzword and approach that consists of devising a lower half or chassis/platform upon which you can then interchangeably place a body type or pod, plus infusing autonomous capabilities into the resultant car. It’s a fascinating notion and one that is worth further analysis — I’ll do so in a moment.

What does this have to do with AI self-driving cars?

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars. The MAVS is a variation of AI self-driving cars and can impact the nature of AI self-driving cars and their advent. It’s worth knowing about and being included into your thinking about the future of AI self-driving cars.

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 self-driving cars less than a Level 5, 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.

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.

MAVS and AI Self-Driving Cars

Returning to the topic of Modular Autonomous Vehicles Systems or MAVS, let’s consider how this relates to AI self-driving cars.

First, one of the most essential questions facing any tech firm or auto maker that wants to develop an AI system for purposes of imbuing a self-driving car is whether to use an existing conventional car as your base or opt to craft something anew.

You might recall that in the earlier days of AI self-driving car efforts there was a tendency toward crafting a new kind of car that would house the AI system. Waymo’s initial efforts went in this direction, and it was long rumored that Apple was aiming to do likewise (as were many other of the auto makers and tech firms in this space).

The logic at the time was that you might as well own and be able to fully control the entire car, soup to nuts, as they say, rather than trying to leverage an existing automobile that you likely otherwise had less control over.

There was also perhaps a bit of bravado involved, sparked by the assumption that it would be nifty to have your own branded car.

Some of this brashness was also due to the origins of many of the self-driving car efforts, namely arising from academic and university settings. In such an environment, you often piece together your own prototype, at times entirely from scratch. This led many of the AI developers that shifted into industry to assume they would do likewise at the auto maker or tech firm that landed them.

The harsh reality is that making a car that can be mass produced, and which can withstand the rigors of daily use in the real-world, requires a lot more effort and knowledge than it does to make a prototype in a research lab.

One concern too was that you would be splitting your limited attention and resources toward trying to solve two different problems at the same time.You are trying to develop and field the AI autonomous systems aspects, which of itself is absolutely a moonshot regarding trying to get to a Level 5 true AI self-driving car.

That also brings up another difficulty about trying to solve two problems at once. If you did decide to make your own new car, when things go awry, how can you discern whether the problem exists in the arena of the car versus in the arena of the AI system. It would be overly complicated to do so.

For a slew of such reasons, most of the tech firms have banded together with auto makers that make cars.

Ideally, the AI systems aspects can be ported over to other models of cars, though this has yet to be seen as a readily possible aspect. Right now, the goal is pretty much to get a car working that has the possibility of becoming a true AI self-driving car, and whatever you choose as the base for now is fine. You can worry about the reusability once you’ve gotten the AI systems to work as intended.

I don’t want to leave you with the impression that the base car can be anything that you willy-nilly choose. As mentioned, the base car should at least be something that works and has a track record of working.

When I say this, I know that some tech firms and auto makers would argue that they would rather start fresh and redesign a car to be suitable for AI self-driving car purposes. Yes, that’s a certainly attractive approach. We already know and acknowledge that true AI self-driving cars will have quite different interiors, since there won’t be a need for a human driver’s position anymore.

In that case, if you are building upon any existing car, one that is being sold to be used on our roadways today, there is obviously going to be an entire setup for a human driver. It’s only the concept cars that showcase the lack of a driver’s position. It makes little sense today to mass produce a car that has no human driving capacity, since who would buy it? Pretty much nobody, since the car would be little more than a lofty looking paperweight.

Modular Software Structure Easier for Porting AI System

For some AI developers, if they aren’t structuring their software in a modular fashion, it is going to be a devil of a time to port their AI system over to some other kind of car. The odds are that their monolith AI has a ton of embedded assumptions about the underlying car that was originally used. Finding those assumptions will be like finding a needle in a haystack.

Whether the AI developers were able to think ahead and get ready for a future of porting their AI would be partially based on not just their own skills, but also on the auto maker or tech firm itself. If the auto maker or tech firm is in a pell-mell rush to be the first to achieve a true AI self-driving car, they might apply such immense pressure to the AI development efforts that anything that seems to cause a delay or inhibit progress gets tossed aside.

I’m not suggesting that the strive to be first is mistaken. It could be that the first to succeed gets all the glory and might capture the market, in which case they can deal with the porting aspects later on. The first such true AI self-driving cars are likely to gain huge acclaim and attention. The headlines will herald as heroes the auto maker and tech firm that managed to achieve the vaunted goal.

Generally, few though tend to believe that being first is an applicable first-mover’s advantage in the case of AI self-driving cars. There are many instances in the tech field of those getting to a new technology first that ultimately were not the “winners” and found themselves eclipsed by those that came along after them.

Some claim that the first true AI self-driving car maker is actually going to likely be the one that gets the most arrows in their back, presumably since their AI self-driving car has gotten into untoward circumstances while attempting to get it market ready.

Let’s try to look at the question in a different light. I’m going to tie things back to the topic of modular vehicles.

Maybe Cram the AI Into the Lower Layer

Rather than making an AI self-driving car by using a whole car, suppose instead you focused on a modular vehicle, specifically a car that is divided into a lower half for the chassis or platform and had a top layer for the pod or body type.

One approach would be to cram all of the AI self-driving car elements into the lower layer. In this manner, you could then mix-and-match other top layers of pods. Those pods would not presumably need any kind of AI capability per se, in terms of the mobility for the self-driving car. This makes it simpler to make different kinds of pods.

This approach assumes that you can have the AI self-driving car capabilities self-contained into the chassis. Maybe this is feasible, maybe not.

For example, let’s assume you are going to use radar, LIDAR, ultrasonic sensors, cameras and the like, all part of the sensory apparatus needed to enable the self-driving car and the AI to navigate and analyze the environment around the self-driving car. Where will those sensors be?

If you put all of those sensors solely into the chassis, this might be problematic. The LIDAR usually needs to be on top of the entire vehicle and be given a 360-degree unimpeded ability to scan the environment. Many of the other sensors are likewise most effective when placed at a position higher up on a vehicle.

As such, you might have little choice other than to split the AI system across both the chassis and the pod. You might be able to put most of the guts of the AI system into the chassis, and then have the so-called peripheral devices like some of the sensors built into the pod. Some of the sensors might reside in the chassis and some of the sensors would reside in the pod.

In that case, you need to ensure that all of the pods have those sensory devices baked into them. You also need to make sure that however you connect the chassis and the respective pod will allow for the interfacing of the sensors that are on the pod with the guts of the AI system that resides in the chassis. One possible downside would be any latency introduced by using that connecting interface. Though, you could make the same argument about even a conventional car that has an “integrated” AI system, meaning that how the sensors are interconnected might suffer a similar kind of latency.

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Can the chassis by itself roll around and act like a self-driving car? It depends once again on the nature of the sensors and where those sensors are placed.

Speaking about the pods, we would now have the handy aspect that we could have one kind of pod that accommodates human passengers, and have a different pod that accommodates goods transportation. This does away with the earlier problem of trying to make one car that can do two different things.

The nature of the removal or disconnecting of the pods, along with the coupling or adding on of the pods to the chassis will be crucial in this setup. If it takes hours to make the switchover of each AI self-driving car, you’ll be in a quandary. Is it better to lose the potential revenue of those AI self-driving cars doing their ridesharing in order to “waste” the presumably no-income time of the switchover, in hopes that the money you’ll make for those AI self-driving cars as cargo carriers will be sufficient?

This kind of switching is only going to make dollars-and-sense if the switch over is relatively fast and easy to do. If the switchover is long to do, that’s a downside.

On the topic of safety, it will be crucial too how the pods are attached to the chassis.

This brings up some notable aspects about the Machine Learning or Deep Learning that might be different when aiming at a Modular Autonomous Vehicle System. Do you train the Machine Learning or Deep Learning on a particular kind of pod, such as the passenger pod, and then it is instantiated when that pod is placed onto the chassis, and do likewise separately for the cargo pod, or do you mesh together the Machine Learning and Deep Learning for both types of pods?

Indeed, I would argue that the entire AI system of the self-driving car would need to be architected toward the notion that there are going to be different kinds of pods being used.

When the Ridek was patented, it was envisioned that the buyers might rent the Modek (the chassis) and possibly buy the pod or Ridon. That’s one way to do things. It could be the other way in that you opt to buy the chassis and rent the pod, allowing you to later on switch to a different kind of pod. Or, the entire setup of both the chassis and a pod is sold, perhaps even selling multiple pod types to someone that wants to have a complete set. It’s a mix-and-match opportunity.

Shifting gears, let’s briefly consider the matter of having a multitude of these MAVS and how they might work together as a team of virtualized ground-based mobility devices.

I’ve mentioned previously in my writing and speaking that we’ll gradually be leveraging a kind of Swarm Intelligence (SI) by having interacting AI self-driving cars. This brings together advances in Distributed AI (sometimes abbreviated as either DAI or DI), along with the efforts of dealing with Multi-Robotic Systems (MRS). In the case of AI self-driving cars, assuming we can achieve individualized behaviors that are sufficient, the next step would be to have them working together in various ways.

For example, you might have them sharing roadway and infrastructure information with each other, allowing any single AI self-driving car to achieve what I call omnipresence. You might have the AI self-driving cars aiding each other in a pack or group manner. This could include a caravan of AI self-driving cars and/or the use of AI self-driving cars for platooning purposes.

Mark Crawford provided a grand vision at AV19 of multiple interacting MAVS that would collaborate with each other. This is the kind of visionary perspective that we need to be thinking about and I applaud him for his efforts in progressing on these matters.

I realize that for many of the auto makers and tech firms it is hard right now to be taking a macroscopic view when you are in the trenches and trying to get your AI self-driving cars to work. Ultimately, it is crucial that we begin identifying a future that we can layout, and be creating a path now to build toward that future.


Dividing up a car into a lower layer and an upper layer might be handy twofer. You can mix-and-match the bottom layer with a multitude of different use-case pods.

To some degree, this dual layered approach enters into an unknown territory for cars. Modular vehicles are taking a bold step toward the future by becoming autonomous modular vehicles.

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Copyright 2019 Dr. Lance Eliot

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Dr. Lance B. Eliot is a renowned global expert on AI, Stanford Fellow at Stanford University, was a professor at USC, headed an AI Lab, top exec at a major VC.

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