Business Operations On Autopilot: 6 Ways To Overcome The Challenges In Operating Autonomous Vehicles

This is probably one of the most exciting chapters of our Digital Transformation In The Automotive Sector Guide series. In all our comic books, movies, literary fiction, songs and more, we have imagined futuristic cars to be autonomous ones – the ones that drove by themselves, transporting passengers without requiring a driver.

Today, autonomous cars don't just exist on paper or prototype models. They exist as fully functional models that are on the roads of the Silicon Valley. From Uber to Tesla, every tech market player is experimenting, building and rolling out a unique version of autonomous cars for a myriad of purposes.

But why did it take so many years after the industrial revolution for us to develop self-driving cars?

Aren't they all similar with just added few modules?

How difficult is it to develop and deploy an autonomous vehicle?

Well, we are about to find out answers to all this in today's post. Like we've been doing in our previous chapters on connected cars, telematics, vehicle sharing systems and more, we will explore in depth the concept of autonomous vehicles and identify some of the most plaguing concerns hindering their commercial deployment today.

So, what are we waiting for? Let's get started.

Important Numbers on Autonomous Vehicles

• In 2019, the autonomous vehicles market was valued at around $24.10bn.
• Experts anticipate an acceleration in the growth of this industry's CAGR by 18.6% between 2025 and 2025.
• North America contributes to around 29% of the overall volume of self-driving cars in the world.
• Reports also reveal that close to 56% of the riders don't feel safe in an autonomous vehicle.
• Google owns over 50 self-driving cars.

What Is The Difference Between Connected Cars and Autonomous Cars?

For the uninitiated, both might appear to be the same because both are powered by the Internet of things. In reality, they are poles apart. Connected cars are those that are connected to the internet to consistently generate and process dynamic vehicle data across diverse parameters to assist drivers make better driving decisions. We have extensively covered connected cars in one of our previous chapters. You could check that out as well.

On the other hand, autonomous cars are concepting that cars that do not have a driver in the first place. All the data generated every single second is used to make the car take better and informed driving decisions in real time.

But when we are talking about connected and autonomous cars, one concept that is common and overlapping is ADAS, abbreviated as Advanced Driving Assistance System.

What Is ADAS?

This is a system in autonomous and connected vehicles that ensures the car, the road and the riders are safe and that vehicles are driven meaningfully by taking care of tons of factors into consideration while driving. Now, this technology can either be deployed to assist drivers make better decisions while driving or make cars take swift actions in real time. To ensure perpetual safety and convenience, ADAS systems usually involve the following modules in their ecosystems.

• Digital side mirrors
• parking assistance
• digital rear-view mirror
• environment mapping
• rear collision warning
• cross traffic alert
• lane departure warning
• traffic sign recognition
• emergency braking
• pedestrian recognition
• adaptive cruise control and more

ADAS modules use human-machine interface systems to translate data into information for better comprehension. Today, even commercial vehicles come with a basic adaptation of the ADAS module such as parking assistance, adaptive cruise control and more.

Challenges Involved In Operating An Autonomous Vehicle

If commercial cars that require drivers pose tons of different challenges to manufacturers, one can only imagine the scale of concerns and shortcomings autonomous cars would pose for vehicle manufacturers. To give you a quick idea, here are some of the most common challenges.

Predicting Real-Time Road Situation

The road is a place with infinite possibilities. And to be prepared for all of them is humanly impossible. Or in this case, training an algorithm to be prepared for situations is technically impossible. That's why it is one of the most difficult challenges to teach the systems to teach how to respond to different road situations so no property or life is harmed in any given circumstance.

An autonomous vehicle should be able to detect and respond to cornering, sudden piling up of vehicles on freeways, unprecedented crossing of wildlife or animals, detect pedestrians trying to cross the road in distress, potholes and bad road conditions, closed roads, roads under constructions and more. And the most important challenge is vehicles only have a few seconds to detect road situations and make an appropriate decision.

Unable To Fetch Remote Updates

For seamless and uninterrupted driving, self-driving cars should consistently receive updated information on a myriad of factors just we like we discussed above. When even one update fails to reach the system, it falters the entire decision-making abilities of the car, leading to unnecessary consequences.

For instance, if the navigation system does not receive information on an approaching closed road, the vehicle might assume that the road is just fine and continue to accelerate towards it. This could result in collisions and cause damage to life and property. Similar situations could also happen when the vehicle fails to receive updates on crossing pedestrians or cornering vehicles.

Environmental Issues

Apart from road conditions, autonomous vehicles should also detect environmental conditions to modify their driving decisions and tactics accordingly. For instance, a self-driving car should automatically detect rainfall and limit cruising at a lower speed to ensure there are no accidents caused due to friction.

It should also detect similar weather changes and take timely decisions for a smooth ride. Another instance is when the car automatically modifies its air conditioner temperature by evaluating the temperatures prevailing outside.

On Road Safety

Because autonomous cars are devoid of drivers to take timely actions, safety is a major challenge in operating an autonomous vehicle. Even today, not all the self-driving cars that are being experimented and tested are completely autonomous. They have modules for manual intervention.

And for those who didn't know, Tesla's autopilot feature also led to one fatality in California. With the trust on autonomous vehicles still being on comparatively low levels, 70% of the riders want manual brakes installed in autonomous vehicles.

This is crucial because an autonomous car is made up of a network of gadgets and devices that are dependent on each other and an overall trust that every module would do its job just right.

Autonomous Driving Experience

The experience of being in an autonomous vehicle should be as seamless as driving a conventional car. This means that a self-driving vehicle should retain all the complexities involved in generating and processing data at the backend and keep the seamlessness intact for riders and passengers. Slowing down of vehicle every time more adequate data is required for an action to happen is life-threatening on the road. An autonomous vehicle should feel like there's a trusted driver on the pedal.

Managing Huge Volume Of Data

This is probably the biggest challenge in the spectrum. Self-driving cars generate and process massive chunks of data every single second. An array of sensors and devices operate on a massive scale to detect and generate data from environments.

This includes LIDAR, RADAR, SONAR, GPS, computer vision, and more. For the uninitiated, an eight-hour drive in an autonomous vehicle can generate up to 100 (TB) terabytes of data.

This translates to the requirement of massive volumes of cloud storage, transmission and processing capabilities with almost 100% uptime perpetually. Efficiency is an understatement as far as the collection, transmission, offloading, storage, interpretation, annotation and algorithmic processing of data is concerned. All these functionalities must be top-notch for vehicles to behave sensible.

Solutions To These Himalayan Challenges

If you've been following us, you would know that we claim solutions to be simple for most of the challenges we discuss in our chapters. But as far as autonomous vehicles are concerned, the solutions are equally stringent to deploy and integrate and you need the best of developers and veterans working on automation systems, data processing, embedded systems and cloud operations for optimum car performance. Because this concept is still evolving, we have identified solutions to several concerns we discussed above. These are currently tried and tested for functionalities.

V2X Connectivity

V2X connectivity refers to the connectivity of a car with its surroundings and other vehicles. V2X technology immensely helps in reducing risks involved in running a self-driving car by giving it the ability to detect objects and elements in an infrastructure or environment.

V2X also integrates with telematics for advanced data processing. Besides, it also switches among multiple operational modules to send and receive appropriate data at all times. One of the best features of V2X connectivity is that it deploys industry-standard encryption protocols of all of the data it generates and its embedded systems.

ADAS Controls

ADAS controls let autonomous vehicles take the most appropriate driving decisions from the accurate data that is fed to it. This includes taking the most relevant and congestion-less route in navigation, automatic parking in narrow lanes, detecting pedestrians and traffic signals from a distance, predicting changing weather conditions and more.

Data Layer Components

In simple words, data layer components are layers of an application that offer access to the data hosted in a persisting storage such as cloud. This enables functioning modules to access some of the most recurring sets of data for real-time processing. This technically ensures less duplication in redundant data from being generated.

HD Map Components

Maps in autonomous vehicles are unlike anything that we know of. They are not graphical or satellite versions of the roads, terrains and routes that we are used to. They are high-definition images or videos of our surroundings that have components detecting every single element in an environment for sensing and processing. These systems differentiate a mailbox from another car and zebra crossings for lane changes avenues. These help cars make appropriate driving decisions.

ADAS Testing

ADAS testing allows vehicles to have a repository of driving decisions in them even before they hit the road. This is because they have been through rounds and rounds of testing under controlled and uncontrolled conditions for algorithms to learn and adapt. Right from the designing stage to production, ADAS modules are optimized for efficiency and precision detection that only continue to evolve even after they hit the road.

Car-as-a-Service

Though cars are fascinating and provide optimum convenience, let's all agree that not everyone in the world wants to own a car. Considering their expenses on maintenance and insurance premiums, we all want cars that require less commitments. Well, that could be easily solved with the Car-as-a-Service model.

This is like Netflix but for cars.

Users can make use of subscription models to use a car and return when validity is over or choose to renew their plans. With a complete ecosystem of fully-functional modules and devices, car-as-a-service could also be a business model ventures can adopt and make it big.

Case Study

Fans of the show Silicon Valley would be familiar with Waymo – a self-driving on-demand cab service in the US. Tested and launched in Arizona, Phoenix, Waymo arrives as one of the first self-driving cab services in the country.

Waymo operates as an entity under Alphabet and it rolled out a fleet of commercial autonomous vehicles that were limited edition experiences. Users had to sign up on Waymo website to be part of the rider program and they would be able to demand rides once their waitlist is approved.

A couple of years back, Waymo also launched dedicated apps for iOS and Android respectively, using which users could have an idea of self-driving cars in their vicinity and demand rides.

Though Waymo deployed a human driver just for backup purposes, it released a fleet of fully autonomous cars in November 2019.

One of the supporting features to assist people get familiar with self-driving cars was that they could honk their cars from their apps. This allowed them to exactly find out where their cars were in public parking spaces.

Waymo is also classified as a Level 4 autonomous car. Developers and experts reveal that the car mimics the five sensory perceptions of humans through its sensor suite that includes cameras, RADAR, LIDAR, GPS and microphones.

When the company realized that there were no vendors or solutions to provide them with the necessary equipment and peripherals, it decided to manufacture them in-house.

As of October 2020, Waymo operates a full fleet of commercial self-driving cars in Arizona. 

Future of Autonomous Cars

To explore the future of autonomous cars would sound too ambitious or even pretentious. People who take interests in science and technology would understand that autonomous cars by themselves are futuristic concepts. We are only testing the water with our current models and prototypes. A lot of associated tech required to make self-driving cars completely safe are still evolving rapidly.

From legal perspectives, several countries around the world are waking up to the potential of self-driving cars and assessing their effectiveness and addressing safety concerns. Based on results and inferences, road laws will be amended in the future to accommodate autonomous vehicles. Newer compliances and protocols will be rolled out and limitations will be placed to ensure optimum road safety. Insurance companies will also modify their policies for self-driving cars.

Now coming to the important aspect, self-driving cars can be classified into 5 different levels:

• Level 1 – driver assisted cars, where computational systems assist drivers drive more conveniently
• Level 2 – partial automation, where drivers can be present on the driving seat while the car drives by itself
• Level 3 – limited self-driving, where cars perform more than two functions at once
• Level 4 – complete automation, where vehicles only need terminal and destination details and they can drive by themselves
• Level 5 – Full-fledged automation, where they can take their own driving decisions, communicate with other self-driving vehicles and take diverse shapes and sizes

Experts believe that only Level 1 and 2 cars will be more available for use in the upcoming decade. Advanced telematics and other tech deployment are required for us to reach Level 3 and 4 automation phases, which is the most futuristic aspect as far as self-driving vehicles are concerned.

Wrapping Up

Despite being ambitious, the autonomous vehicle spectrum still requires credible solutions to most of the challenges it faces today. Companies are looking for dynamic and instant integration of solutions for their vehicles.

And this is why it is ideal to get started with an ideal self-driving car module such as advanced telematics, V2X solutions, ADAS control units and more for the cars of tomorrow. If you intend to get automotive software solutions developed for self-driving vehicles, get in touch with us today.