The Obstacles of Autonomous Driving
By: Beyond Boundaries
Tags: Autonomous Cars, Engineering, AI, Emc

Does this sound too good to be true? It doesn‘t have to because, in the age of deep learning and neural networks, this is a graspable achievement — even very soon. First of all, let us clearly define the term Autonomous Driving, and more importantly, what is necessary to achieve autonomous driving.

What is Autonomous Driving

The term autonomous is often used in wrong cases, implying functionalities that are technologically not possible yet. An autonomous vehicle or a self-driving car is a vehicle that can navigate through daily traffic without any human input or guidance whatsoever. It is capable of getting the car from point A to point B safely, while the driver doesn‘t have to keep his attention on the road and can concentrate on other things. Hence a truly autonomous vehicle is void of a driver’s workplace or tools (like a steering wheel) in the cabin, and as such is self-governing. It delivers reliable performance under significant uncertainties, without external intervention. Meanwhile, vehicles that are marketed as autonomous have in reality only automated driving capabilities. The vehicle with automated driving capabilities expects and needs external intervention from the driver or artificial aids in the environment. To further digress the automation stages, we can take a look at the different levels of automation.

Level 0 : The vehicle can issue warnings like keeping a proper driving distance or another vehicle in the blind spot of the driver. However, the car has at no time control over the vehicle.

: The vehicle can issue warnings like keeping a proper driving distance or another vehicle in the blind spot of the driver. However, the car has at no time control over the vehicle. Level 1 : This level is known as the hands-on-Level. Control over the vehicle is shared between the driver and the automated system. The system partially controls the car via adaptive cruise control, parking assistance, lane-keeping assistance, etc. The driver must be ready to retake control at any time.

: This level is known as the hands-on-Level. Control over the vehicle is shared between the driver and the automated system. The system partially controls the car via adaptive cruise control, parking assistance, lane-keeping assistance, etc. The driver must be ready to retake control at any time. Level 2 : This level is known as the hands-off-Level. Control of the vehicle is fully handled by the automated system. It determines the acceleration, braking, steering, etc. Although the driver is allowed to take his hands off the wheel, he does have to keep his concentration on the road to regain control of the car when necessary. To ensure the driver’s safety the car system monitors his eyes to determine whether handing over the car is safe.

: This level is known as the hands-off-Level. Control of the vehicle is fully handled by the automated system. It determines the acceleration, braking, steering, etc. Although the driver is allowed to take his hands off the wheel, he does have to keep his concentration on the road to regain control of the car when necessary. To ensure the driver’s safety the car system monitors his eyes to determine whether handing over the car is safe. Level 3: This level is called eyes-off-Level. The system is capable of handling situations of immediate response, like emergency braking, on its own. Although the system is capable of self-reliant driving maneuvers, it is still considered a co-driver. The driver must intervene if need be.

This level is called eyes-off-Level. The system is capable of handling situations of immediate response, like emergency braking, on its own. Although the system is capable of self-reliant driving maneuvers, it is still considered a co-driver. The driver must intervene if need be. Level 4 : This level is known as the mind-off-Level. Different from the previous level, the driver’s attention is not needed to ensure a safe journey. The vehicle has to move within certain spatial areas to enable this level of automation. This is known as ‚geofencing‘, which describes a virtual perimeter of a real-world geographic area. It allows the vehicle to communicate with other vehicles and artificial aids in the area to ensure safe transportation.

: This level is known as the mind-off-Level. Different from the previous level, the driver’s attention is not needed to ensure a safe journey. The vehicle has to move within certain spatial areas to enable this level of automation. This is known as ‚geofencing‘, which describes a virtual perimeter of a real-world geographic area. It allows the vehicle to communicate with other vehicles and artificial aids in the area to ensure safe transportation. Level 5: The steering-wheel-optional-Level. Only vehicles with this level of automation are truly autonomous. As the name suggests in the cabin, there is no differentiation between the driver and the passengers, as there is no wheel or pedals to control the vehicle’s driving parameters. The robotic vehicle is self-sufficient at any given time, which renders driver intervention obsolete.

As we see, only the fifth level of automation requires no human input at all. This is why the Original Equipment Manufacturer’s (OEM’s) marketing of a self-driving car is misleading. Every released autonomous car has a steering wheel equipped. Thus the input of a human driver is expected and needed. Even a Tesla car is a highly automated vehicle at best and not an autonomous car as by definition. What is it that stops us from driving in level-5-automated-cars? Let us take a good look at the challenges and limitations that stays between us and self-driving cars.

Limitations and Challenges of Autonomous Driving

As with every new technology, there is a transition phase that brings new challenges to the table. Remember when Apple left out the headphone jack on the iPhone 7 in 2016? What a shit storm that was. We had to use adapters and a wireless technology called Bluetooth but we somehow managed to get to the year 2020. Now everybody is running around with a pair of AirPods. To be fair autonomous driving is a bit more complicated than a missing port, but we still face the same main two challenges. These spring from the technological side and the human side of things.

First of all, there are the driving limitations of an autonomous vehicle. Artificial intelligence (AI) is not advanced enough to handle chaotic inner-city traffic. Instability is further promoted through the ability to compromise the car’s computer or communication between two cars and the susceptibility of the car’s sensors and navigation systems to different weather conditions like snow or heavy rain. Additionally, sensors could be deliberately compromised, e.g. by painting over camera lenses of the car’s navigation system.

The car’s AI must be able to detect animals of any size and react accordingly to prevent accidents with wildlife. A good example of this weakness can be seen in one of George Hotz’s streams, where George debugs his machine learning framework ‘tinygrad’ because it detects a fish when given a picture of a cat. The problem with this anomaly is that the car will trigger a different reaction protocol than needed. On this matter, Volvo found that software suited to caribous, deer, and elk was ineffective with kangaroos.

Another hindrance for autonomous vehicles is outdated maps. Let’s assume that on our frequently used highway a construction site was erected overnight. Of course, online maps are not updated yet, but a new speed limit is already placed. Since autonomous cars navigate with online maps, they wouldn’t slow down at a construction site. The driving robot would need a reasonable behavior script to fall back on. A transmitter could supply autonomous and automated vehicles with the needed tempo limit, construction site length, etc. Although working with transmitters can cause the problem of communication interference with spectrums desired for the car radio.

Even if we manage to overcome the technological challenges there are still social challenges we have to face. Where new possibilities emerge, there need to be regulations that keep humans from misusing these possibilities. Since autonomous vehicles share the same roads as standard cars that are governed by rules and regulations there must be regulations for them as well. Until regulations are released, there will be uncertainty, and uncertainty hinders or delays the deployment of automated cars. It’s the same with Elon Musk’s Gigafactory in Germany. It’s only possible to execute the construction at such a fast pace because nobody waited for construction authorization from the local government. The downside to this is that if the government finds any error in the building plans or violation of the local regulations, they can demand to either halt further construction work or even deconstruct what is already build. As such, nobody wants to take a risk.

Another significant factor is worker balance. Autonomous cars create two problems with employment at large. Firstly, those that drive a vehicle professionally, like cab drivers, bus drivers, or truck drivers, could be unemployed since the driving robot can drive 24/7 with no payment and drive at a much safer rate. In the long term, a driving robot could save big companies a substantial amount of money.

Secondly, the demand for research and development staff in leading companies is immense. Because official education in disciplines like machine learning and artificial intelligence is scarce, there is an enormous gap between demand and supply. Thus, slowing down the deployment of autonomous vehicles even further. Meanwhile, third-party organizations such as providers of online courses and self-taught community-driven projects such as DIY Robocars and Formula Pi have grown in popularity.

The last social problem, which is more psychologically based, is the simple fact that some people don’t want to give up their control over a machine. The same phenomenon occurred when the automatic transmission saw its first rise in popularity. Then convenience won, and in the meantime, the automatic gearbox became part of the standard equipment of most cars nowadays.

Oftentimes, this convenience comes with a cost — security. Let’s take a look at how secure autonomous and automated vehicles, in theory, are.

Security of Autonomous Driving

The first argument which comes to mind is the ‘hackability’ of autonomous vehicles. What happens when a skilled hacker successfully gets into the heart and mind of an autonomously driven car? Shortly put, the consequences are somewhat frightening, considering that a fully automated vehicle has no steering wheel or pedals for manual input. The hacker could easily decide the direction and speed of the car, potentially crashing the car and its passengers into the next tree. This was already demonstrated, although to a much lesser extent in 2015. Two hackers were able to manipulate the AC, windshield wipers, and radio before shutting off the engine of a Jeep Cherokee from 10 miles distance, as automotive journalist Scott Huntington reports.

Naturally, every gap can be filled, even in software. However, this requires programming professionals, of whom there are only a few. So the demand for programmers won’t decrease anytime soon.

Still, there remains one big problem that we can see on the engineering side, which is being neglected too often and should be particularly stressed in this blog. It is about the electromagnetic compatibility, or short EMC, of autonomous vehicles. Every computer or electronically driven device is prone to disruptions due to electromagnetic interferences in its vicinity. May they be triggered by other electronic devices or the device itself. Consider this example. Let’s assume that we are driving in our car, and our phone starts ringing as we get a call from our spouse. As the phone’s signal is at its maximum when being called, the electromagnetic interference field it emits is also at its maximum. Thus, disturbing other devices in its surroundings. In our car, the chip which triggers the airbag is influenced by the phone’s magnetic field, thus triggering the airbag to inflate. Let’s now translate this problem to an autonomous vehicle. What would happen if our car drives into a strong electromagnetic field? Since the driving capabilities largely depend on the communication between sensors within the car, a disrupted sensor could detect a car in front of our car, where in reality there is no car. Hence, it initiates an emergency brake, causing the other car behind us to drive into our rear unchecked.

This is a rather difficult matter. Although we can filter electromagnetic disturbances, the intensity of the interruption and even its emergence in the real world is dependent on the frequency of the anomaly. For example, we could ensure immunity for electromagnetic interferences at 30 kHz. However, we would be completely vulnerable at 90 kHz. A delicate matter to be sure, but similar to the security issues, this is nothing which can’t be achieved.

Since the consequences for malfunctioning autonomous vehicles are so severe, we need to be completely sure to cut off any possible error sources. Only then can we use driving robots in everyday life without hesitation. Eventually, this will be our reality, but the path until we get there will be long and probably unpleasant.