Barriers to Autonomous Vehicle Adoption

Autonomous driving has been a futuristic technology we’ve seen in entertainment for decades. In the 2020s, self-driving seems like it is becoming a tangible reality, with manufacturers like Tesla releasing Full-Self Driving (FSD) mode, and other manufacturers eager to follow suit. While manufacturers have quite a way to go before achieving high or full automation (level 4 and 5, respectively, according to the SAE levels of vehicle autonomy), the bigger problem is that though manufacturers may be hard at work developing autonomous technology for future self-driving vehicles, there are still many factors to consider before fully overcoming the barriers to adoption of autonomous vehicles. Here are just a few of these challenges we look through in our blog today.  

1. Technology

There are a variety of factors why we haven’t seen more autonomous vehicles on our roads – technology is one of them. As previously mentioned, though manufacturers may tout “autonomous tech,” it’s still more of a marketing term, rather than technologically reliable. For example, autonomous vehicles rely on sensors like Lidar, Radar, and cameras. These technologies help the car navigate the environment around them, and they should be able to detect buildings, other vehicles, road infrastructure (think traffic signals and road signs) and most importantly, pedestrians. Sensors have to “see” in order to do all of this properly: whether it’s in inclement weather conditions, or in congested urban areas. While the available sensors are continuing to make great advancements in ensuring safety in autonomous driving, we have to ask ourselves what is the level of certainty we require and the level of risk we’re willing to allow.  

2. Public acceptance

In addition to the technology development, there is the issue of acceptance within society. While the majority of the population enjoys seeing futuristic technology on the big screen, it is an entirely different matter when it comes to personal usage. There is still resistance and distrust when it comes to self-driving cars. The Partners for Automated Vehicle Education (PAVE) found that 3 in 4 Americans say that they don’t think autonomous vehicle technology is ready to be mainstream. 48% said that they would never use a self-driving ride service, and 20% believed that AVs will never be safe. This was a 2020 poll, but polls in previous years have shown similar skepticism, and it looks like this attitude might be here to stay unless some major changes occur.  

3. Security

One reason that the public is still wary of autonomous vehicles may be that they don’t fully trust the security of AVs. More connected than its traditional counterparts, an AV is essentially a smartphone on wheels, which means that it is at risk of breaches unless a proper cybersecurity management system protocol is in place. There isn’t a one-stop solution to having a proper cybersecurity management system, but the ISO/SAE 21434 risk assessment system is a good place to start.  

Fortunately, manufacturers will soon be mandated to have a proper cybersecurity system in place. Starting in July 2022, new vehicle models will be required to get cybersecurity type approval for the model before it is allowed to be put on the market.  

4. Standardization and regulations

Ultimately, many of our aforementioned barriers to adoption can be improved upon when there are wider, universal standards put into place by regulators. For example, the UNECE’s WP.29 regulations for cybersecurity will mandate in-vehicle security for new vehicle models next year, and all vehicle models by 2024. With this kind of overarching, nearly-universal regulation in place, the public’s acceptance of connected and autonomous vehicles will continue to grow.  

An added example is the recently published ISO 22737, the first international safety standard for Level 4 automated driving systems. These types of standards help us to have more trust for safety and security, addressing the minimum requirements for technology systems, that previously were unclear.  


Unfortunately, AV adoption isn’t as simple as making a fully autonomous vehicle and putting it on the road. To overcome the various barriers to adoption, many factors need to work hand-in-hand to ensure not only that the vehicles do what they’re supposed to do, but also other societal infrastructure is on the same page. Standards will help move this process along, and we will be sure to keep you updated on the latest standardizations that are developed to keep our vehicles and mobility services secure.  

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The Future of the Car: A Paradigm Shift of the Century

A key characteristic of the fourth industrial revolution is that conventional machines and electronics are reinvented or combined into “smarter” all-in-one products, blurring their original definitions. For instance, the smartphone was reinvented by combining a conventional cellphone and a computing device. The smartwatch was created by combining a conventional watch and a computing device. The smart speaker was a combination of a conventional speaker and a computing device. The list goes on. Instead of drawing new things out of scratch, the fourth industrial revolution seems more like an overhaul to our existing world, where we reinvent existing items and redefine their purposes, often by combining them with computing capabilities and connecting them to the cloud. What’s more interesting is how people’s perceptions and attitudes towards these products change as they experience and interact with them. Since these reinvented products tend to serve a variety of purposes that overlap with one another, users have more options available at their hands to do the tasks needed, making daily lives more seamless.

The automotive industry is no exception. However, changes here are less visible as they occur at a slower pace. Perhaps it is because cars are relatively expensive items with longer lifecycles, or because cars directly determine our physical safety, or that cars have been around for much longer compared to other electronic devices and appliances. Indeed, since the world’s first engine-powered vehicle was invented by Carl Benz in 1885, essentially the same car concept has been with us for more than a century now. Yes, the appearance of cars has evolved considerably, but their functionalities and benefits have remained unchanged. For over a century, people have viewed the car as a mode of transportation for people and goods from point A to point B.

With the fourth industrial revolution, we are finally starting to witness a change to the century-old definition of the car. This enormous paradigm shift can be characterized by several seemingly unrelated industry trends.

2000s: Car Tech

For many decades, the only digital technology the average car had was the radio. Yet, starting in the 2000s, new technologies began to emerge, one after the other. From GPS navigation to Bluetooth, hands-free calls to voice command, phone mirroring to video streaming, the car had become a sophisticated computer with countless features.

As people interacted with these new features, their perceptions and expectations changed. These changes made it more challenging than ever for automakers to build a satisfactory car. In the past, a car was judged only by quality, comfort, and performance. Excelling any two of the three aspects would pretty much guarantee success. This was how big and prestigious automakers survived all these years of competition. But even the big names are facing difficulties today because consumers are so used to car tech and demand more and more of these tech features manifested in the most intuitive and useable manner.

The increased demand for car tech signaled the beginning of the paradigm shift; cars were no longer a simple means of transportation, but an experience to enjoy.

2010s: The Growing Popularity of SUVs

This is by far the most visible change that can be easily observed by anyone attentive to the road – sedans are being taken over by SUVs. Almost every automaker worldwide has reduced sedan lineups, favoring prioritization of the rollout of SUVs. Even OEMs that traditionally focus on the niche market are now abandoning sedans and moving to SUVs as an attempt to capture the mass market. Porsche is a typical case where the brand repositioned itself from a sports car brand to a brand focused on luxury SUVs. Even Rolls Royce, Bentley, and exotic makers like Lamborghini are adding SUVs into their flagship lineups.

Statistically, the market share of SUVs has increased dramatically over the past decade. Between 2010 and 2019, the global market share of SUVs in total car sales increased from 17% to 41%, with the figure reaching as high as 50% in the US. In a matter of a decade, SUVs have become the most popular car segment on every continent.

Why are SUVs becoming more popular? While there are many hypotheses, most of them point to a change in the general public’s perception. SUVs can make people feel more powerful, and while sedans are built with performance in mind, SUVs allow for more space and a greater onboard experience, rather than the drive itself. Therefore, since the paradigm of the car is shifting away from driving and more towards the onboard experience, there are simply fewer and fewer reasons to buy sedans over SUVs.

2020s: Environmental Responsibility

For decades, cars have been blamed as a major culprit for climate change and global warming. This forced the industry to seek more sustainable options, going from gasoline to hybrids and now towards electric. The electrification trend is less related to the car itself, but more of a result of external pressure.

Why has the electric car gained popularity in such a short period of time? This can be attributed to multiple reasons, such as better battery technology, success in Tesla’s marketing campaigns, and increased environmental awareness worldwide. But the most critical reason behind this trend is that people are gradually seeing cars as more of an innovative tech than a conventional machine. Since the paradigm shift has already blurred the definition of the car and changed public perception of what a car should be like, it is now a lot easier for people to adopt electric vehicles. It is also easier for EV makers to experiment with bold and exotic designs.

An interesting phenomenon is that the more people interact with electric cars, the more their perceptions of the car will shift towards them. This again further accelerates the process of EV adoption. Based on this effect, it certainly won’t be long before EVs surpass ACE vehicles in sales.

2020s: Autonomous Driving

Autonomous driving has been one of the most controversial topics in the automotive industry due to a wide range of concerns on safety and legality. Now, with the advancement of big data and artificial intelligence, along with the increased stability of the cellular network, the public is now finally ready to trust the car to drive itself. Even though most of the current semi-autonomous vehicles rely on cameras and sensors, this is about to change as V2X technology starts to roll out in newer vehicles. When V2N technology gets adopted by the mid-2020s, many of the vehicles on the road are expected to reach full autonomy.

Again, the public’s increased acceptance for autonomous driving is not only due to technological advancement, but rather, caused by the paradigm shift. Reemphasizing the point that cars are now more associated with their onboard experience rather than the driving experience, people are more willing to let the car do the driving and focus themselves on the cabin experience.

2020s: Mobility as a Service

The paradigm shift has redefined the car to become less of a transportation tool and more of a mobility experience. Now some may ask, what about those who only want a simple transportation tool without having to own a bunch of add-on features? Those needs can be answered by a new market: mobility-as-a-service (MaaS).

For those who choose to not purchase the complete experience and only want a minimalistic ride, MaaS is becoming an appealing alternative to owning a car. With the help of big data and machine learning, ride-hailing and ridesharing services are becoming increasingly popular among those who do not like owning cars. Advanced fleet management systems allow the operator to perfectly match vehicle supply to passenger demand, dispatching the perfect number of vehicles to each area in need, and automatically carpooling those on the same routes. These on-demand services will completely transform public transportation so that people no longer need to look for bus stops and are no longer confined to living near subway lines.

The New Paradigm: A Lifestyle on the Go

In essence, the car is becoming less and less of a transportation tool and more of a mobile home characterized by entertainment, convenience, and comfort. With more and more workers working remotely, people are now having more time and freedom to live and travel to any place they like. The car represents this dynamic lifestyle, offering a private space that feels like home, with all the enjoyment, convenience, and comfort of home. Only automakers that can best adapt to the paradigm shift will survive the 2020s.

When Last Mile Delivery Turns Autonomous – What are the Considerations?

As more of our lives becomes connected to networks and online services, we are finding that previously labor-intensive tasks like getting groceries or using public transportation are becoming streamlined as reservations and payments can be made online, even bringing items to your front door. But with increased connectivity comes greater expectations for faster, efficient, affordable, and secure services and deliveries.

The delivery process may seem straightforward – order your product, the seller arranges products for delivery, and the delivery service brings it to you – the actual process of getting the many different products with different supply chains together in one place, then arrange the transport going from the hub to the final destination. This portion of the trip is called “last-mile delivery” as it’s one of the trickiest parts of the supply chain process. Not only is last mile delivery the part that we as consumers often care the most about, last mile delivery accounts for around 53% of the total cost of shipping and sellers are taking less of a cut as supply chains struggle to meet demand and deadlines.

last mile delivery challenges

(Source: EFT)

Last mile deliveries are often inefficient because of the sheer volume of deliveries necessary with a low drop size. This means that efficiency is virtually impossible with retail sales only going up, especially with a global pandemic.

Taking care of the last-mile challenge

The supply chain management industry experts have been looking for solutions to this dilemma. Many have pointed to models that utilize digital platforms to crowdsource local services to ensure that consumers are able to get what they need. Instead of manually dispatching delivery people to make the trip, platforms will utilize machine learning and AI to quicken this process.

Another solution that many companies are already cashing in on is the concept of autonomous delivery technology, with the market expected to reach 84.72 billion USD by 2030. In order to make last-mile deliveries more efficient and truly door-to-door, development has been focused on autonomous pods, which can navigate more difficult and uneven terrain to ensure completion of delivery.

Smaller pods focus on small, dynamic designs to ensure efficiency in last-mile delivery. This allows for reduced costs in the delivery pod itself, as well as increased security as the vehicles do not actually carry human beings, allowing for more focus on the actual delivery and of the safety of those around them. Especially with the global pandemic, solutions like autonomous delivery allow for contactless service, without driving up costs. Analysts at McKinsey found that semiautonomous and autonomous technology reduce delivery costs on average by approximately 10 to 40 percent.

Autonomous delivery challenges

We can already see this crowdsourcing model mentioned in the previous section in the real world, prevalent in industries like transportation (think ride-hailing apps), food delivery, and retail apps. But this solution isn’t without challenges of its own, as local services still have a high maintenance cost, and with actual human drivers, there’s likely going to be a larger margin of error and limitations.

This is why tech industry experts say that long-term, autonomous driving technology is the answer. Autonomous vehicle technology has made great headway in the last decade – from the testbeds to public roadways — and while use cases are moving from theoretical plans to reality, there’s still a lot of doubt and logistical issues when it comes to application.

Technology and infrastructure limitations

While there are some companies who have gotten the autonomous delivery robot/pod concept to real-world application, there’s still a lot at risk in terms of how the technology works. Passenger vehicles have yet to be at a Level 4 of autonomous driving (according to the SAE regulations), so it’s not completely realistic to expect autonomous pods to be at this level. We need to be careful to ensure that the priority is placed in keeping human beings (pedestrians and vehicle passengers) safe when pods are navigating through streets.

While the technology is being developed, we also must remember that technology can’t be the only thing to change. Cities also need to ensure that current infrastructure supports the autonomous delivery movement, for example, making sure that roads are paved properly and that any obstacles like potholes or cracks are quickly repaired.

Lack of universal standards and liability regulations

To deal with the infrastructure challenges mentioned in the earlier section, there needs to be universal regulations that allow for both services and end users to use services more effortlessly across state or even country-lines.

In countries like the United States where there are differing federal and state regulations, using mobility services like last-mile deliveries with autonomous technologies can be a challenge. For example, in the state of Pennsylvania, autonomous delivery bots are allowed to maneuver their way through sidewalks as well as roadways. They are technically considered “pedestrians” meaning that the bots can move at a maximum speed of 12 miles per hour in a pedestrian area with a load limit of 550 pounds. This isn’t true for all states as some have no regulations regarding delivery systems like this, while others require permits to be issued by the state.

These technicalities can make a major difference when it comes to not only service operations, but also liability frameworks in the case of an accident. As autonomous technology has not yet been perfected, there is risk when it comes to operation no matter how safe the company deems it. A universal standard or regulation will allow for the risk to be minimized, as much as possible.

Risks of security breach

Because of the groundbreaking nature of this technology, many often focus on the issues surrounding the technology itself. However, we must remember that this technology is connected in nature, meaning that thousands of messages containing data are being exchanged each second in each vehicle. With data like PII, vehicle data, as well as access points to connected devices, a successful breach can be a goldmine for malicious actors.

While autonomous delivery vehicles like pods or robots do not carry any passengers, the personal data that they carry, their operations on pedestrian sidewalks, as well as the close nature of door-to-door delivery still carries implications that we must consider before application.

Short and long-term solutions for autonomous delivery

The technology for autonomous delivery bots will continue to progress. But how quickly this happens depends on other factors. In the long run, standards and regulations will have to be made by legislators and committees, which will influence new infrastructure that will enable this kind of technology to have more widespread adoption.

In the short-term, however, both manufacturers of these pods as well as service providers can prioritize security like authentication and encryption, ensuring that the data stays private. Security solutions can be built into the chipsets in the manufacturing stage, protecting data privacy before vehicles, and pods, hit the road. Solutions like these can ensure that vehicles of all sizes protect not just the items carried inside, but also those around them.

The Role of Machine Learning in Strengthening Autonomous Vehicle Security

With Tesla considered one of the best bubble stocks for 2021 (shares soared 743% in 2020 and made Elon Musk the richest person in the world for a few days), the company is at the center of people’s attention as it’s been evolving on a very public stage. While the market indicates an increasing interest in autonomous driving, AAA’s 2019 annual vehicle survey found that 71 percent of Americans are afraid to ride in self-driving vehicles, especially after several high-profile incidents came to light the past few years.

The statistics above suggest that we may still be a few years away from driving fully autonomous cars. For self-driving cars to be fully autonomous, they need to deploy technologies such as RADAR (Radio Detection And Ranging), and LiDAR (Light Detection And Ranging) as well as algorithms to detect and respond to surroundings.

Can Autonomous Vehicles Be More Dangerous Compared to Traditional Vehicles?

Autonomous vehicles can be much more vulnerable than other devices we use in our daily lives as they utilize a combined deployment of various sensors and vehicle-related technologies. It’s known that even a single vulnerability can allow hackers to exploit the entire vehicle – meaning hackers may not only gain access to the operating system but possibly the entire network as well.

What’s more, autopilot has helped set the standard for numerous autonomous vehicles and gave a taste of what self-driving cars will be like in the near future. However, experts at the Tencent Keen Security Lab demonstrated that they could remotely compromise the Autopilot system on a Tesla vehicle. Even though the bug was promptly fixed after the presentation, this situation sheds some light on the potential for exploitation. As autonomous vehicles rely highly on “connectivity” itself, there’s no doubt that hackers see autonomous vehicles as tempting targets that contain countless amounts of data that can be used to exploit the system, which in theory could end up destroying every single aspect of the vehicle.

That is why in-vehicle security and the complexities involved have been the major focus of any discussion about autonomous vehicles. In-vehicle security isn’t just about protecting and securing the autonomous vehicle itself, but rather about mitigating as many risks as possible through the delivery of a comprehensive and holistic approach to automotive driving security.

How Can Autonomous Vehicles be Secured?

In order to secure the whole autonomous driving process, an important fact needs to be emphasized; these vehicles aren’t like the traditional ones out there. The complexity of autonomous vehicles makes it far more difficult to fully secure the vehicle – though it’s not impossible – and the only way to do that is by prioritizing security.

One possible solution is in-Vehicle Security (IVS) which is the car’s first line of defense that helps protect vehicles from external threats, monitors all relevant communications, and responds to any abnormal activity. As a result, deploying IVS is what’s most important in securing the vehicle. IVS needs a reliable Intrusion Detection System (IDS) that provides the security modules needed to guarantee safe communications between Electrical Control Units (ECUs).

Additionally, with the adoption of new regulations, it’s important to make sure that your provider is prepared to meet the requirements of WP.29 along with other industry standards of deploying a system that secures communication between vehicles, devices, and infrastructures.

This is where machine learning comes in.

How Can Machine Learning Enhance the Security of Autonomous Vehicles?

Machine learning is the process of using, storing, and finding patterns within massive amounts of data, which can eventually be fed into algorithms. It’s basically a process of using the data accumulated by the machine or device that allows computers to develop their own algorithm so that humans won’t have to create challenging algorithms manually.

With all the features and applications of machine learning, it’s easy to understand how our collected data are stored and used via a proper platform which in turn analyzes logs and patterns. In this way, this platform can warn and even mitigate risks occurring within the vehicle.

In other words, once the logs are collected and stored, machine learning technology can start analyzing and detecting these logs to see if there are any abnormalities. As machine learning enhances the detection model, it develops algorithms that can be used to detect malware activities and unusual behaviors of the vehicle. This process enhances the driver assistance technology by classifying the right data and patterns through various sensors attached to the vehicle.

Moreover, thanks to the advances in wireless technologies, a vehicular (ad-hoc) network is being formed among moving vehicles or RSUs (Roadside Units) and other communication devices. This network is considered a proprietary system that is seen differently from average computer networks, making it easier to predict the movements of vehicles. Machine learning can be employed in training algorithms from the very beginning to detect malicious exploits by differentiating normal from acute driving behavior which alerts the driver and prevents an attack.

In order to realize this, NXP is taking the lead in manufacturing microcontrollers with AI and machine learning capabilities that can be plugged into the OBD-II port. This not only observes but also allows the device to capture the vehicles’ data patterns to detect and monitor any abnormalities. Once it’s monitored, the microcontroller basically tries to prevent and alert the driver and becomes the replacement for traditional algorithms employed in vehicles.

Autonomous Driving is Not the Distant Future

It’s important to realize that autonomous vehicles that aren’t prioritizing security will cause far more serious consequences that involve physical harm or could even be abused by rogue nations and terrorists that are looking to cause chaos. Therefore, different security technologies must be considered when designing the security architecture from the very beginning.

Also, machine learning can become an essential tool for OEMs, Tier-1 suppliers, or manufacturers that are looking to secure their autonomous vehicle and driving-related resources. After all, the new transportation system will need a total security solution that covers from intelligent transport system to in-vehicle, charging and connections security.

AUTOCRYPT’s Automotive Cybersecurity Solutions

AUTOCRYPT provides a total vehicle security solution that secures all parts of a vehicle by providing various security modules such as firewalls, authentication systems, to secure the vehicle from end to end.

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How Autonomous Vehicles Solve Urban Space Shortage

With more than half of the global population living in cities, space shortage is becoming an increasingly urgent problem for urban planners and developers. The two major challenges that impact urban residents the most are housing shortage and parking space shortage.

Housing shortage can be partially resolved by utilizing vertical space. Indeed, the number of skyscrapers built for residential use is quickly overtaking that of office buildings in major cities across the world from Toronto and Vancouver to Sydney and Melbourne. Yet even doing so is not enough to cool down the housing shortage as non-resident investors drive up demand.

In this blog, we focus on discussing the second challenge. In fact, parking space shortage and traffic congestion are much more difficult to deal with than housing shortage. As one can imagine, building upwards is not an option because it would be ridiculously expensive and inefficient to have a 30-storey parkade. Also, most cities do not have enough public funding to build elevated highways and tunnels at a large scale.

Like it or not, this urban movement is set to continue as the UN projects global urban population to reach 68% by 2050. Just as urban researchers are seeking new creative ways to solve space shortage, an unexpected potential solution has gained popularity both in theory and in practice — autonomous vehicles. Self-driving cars are expected to solve urban space shortage in three different ways: 1) by reducing the need for parking space, 2) by reducing traffic congestion, and 3) by increasing vehicle occupancy rate.

How Do Autonomous Vehicles Reduce the Need for Parking Space?

The idea is to have cars park themselves in the parking lot so that drivers can get off the car at the entrance and let the car do the rest of the job, just like having an automated valet parking system. So how and why does this system reduce the need for parking space?

First of all, each single parking slot for autonomous vehicles can be made much smaller than a conventional parking slot. This is because a conventional parking slot has to leave enough space for the car doors to open on both sides so that passengers can get off. When it comes to autonomous vehicles, the driver and passengers can get off the car ahead of time before the car enters the slot, so that no extra space is needed on the sides of the car.

Another reason is a reduction in the need for driveways. In a conventional parking lot, the driveways take up about half of the total land area (see Figure 1a). Civil engineering researchers at the University of Toronto have shown through their work that autonomous vehicles could potentially decrease the need for parking space by an average of 62% and a maximum of 87% (Nourinehjad, Bahrami, and Roorda 2018)*. The reason is that instead of having a driveway between every two rows of parked vehicles, a parking lot that is fully dedicated to autonomous vehicles only needs a driveway between every four rows of parked vehicles. In other words, there can be up to four rows of vehicles parked together without any driveways in between (see Figure 1b). When a “landlocked” vehicle needs to get out, the vehicle in front of it would automatically move out to free its way.

Figure 1. a) Conventional Parking Lot vs. b) Autonomous Vehicle Parking Lot
(Nourinehjad, Bahrami, and Roorda 2018)*

Nourinejad, M., Bahrami, S., & Roorda, M. J. (2018). Designing parking facilities for autonomous vehicles. Transportation Research Part B: Methodological, 109, 110-127.

How Do Autonomous Vehicles Reduce Traffic Congestion?

A common misconception is that autonomous vehicles are nothing more than cars with sensors that detect surrounding environments. In reality, SAE Level 4 and Level 5 autonomous vehicles are much more sophisticated than that. These vehicles are able to communicate with other vehicles on the road, with pedestrians and cyclists, with traffic lights, and with the entire transportation infrastructure, all through the internet. All the communications are enabled by V2X (vehicle-to-everything) technology embedded into the vehicles, and end up forming a massive smart transportation network. This brings us to the question: how does V2X technology reduce traffic congestion?

Surprisingly, the main cause of traffic congestion is not having too many cars on the road, but the delays caused by each driver’s reaction time. When a traffic light turns green, for example, it takes a second or two for the driver at the front row to notice the signal change and another 0.5 second before pressing the pedal, the driver behind starts pressing the pedal 0.5 second after the first car moves forward, and this 0.5 second delay stacks up for every car behind, accumulating to a significant latency — this is assuming that everyone pays full attention to the road. (We all know that one bad driver who is just too busy on their phone to pay attention to the signal change.)

With V2X technology, all cars waiting in line would be notified of the signal change with near-zero latency, so that all cars can start accelerating at the same time and move forward at the same speed. This would significantly reduce traffic jams. A research team from the Delft University of Technology discovered through their virtual experiment that under a particular traffic jam which lasted an average of 41.7 minutes with an average speed of 11.7 km/h, if only 10% of all vehicles had V2X technology, the average lasting time would be reduced to 3.6 minutes with the average speed increased to 41 km/h. This huge improvement is made possible by all vehicles being able to accelerate and brake at the same time (Wang, Daamen, Hoogendoorn, and Bart van Arem 2015)*.

* Wang, M., Daamen, W., Hoogendoorn, S. P., & van Arem, B. (2015). Cooperative Car-Following Control: Distributed Algorithm and Impact on Moving Jam Features. IEEE Transactions on Intelligent Transportation Systems, 17(5), 1459-1471.

How Do Autonomous Vehicles Increase Occupancy Rate?

According to the National Household Travel Survey conducted by the US Department of Transportation, the average occupancy rate of vehicles on American roads dropped from 1.59 in 1995 to 1.54 in 2007. This means that not only are we having more cars on the road, each car is carrying less people, with a majority of cars on the road occupied by only one person. This low occupancy rate is largely due to the inconvenience of the public transit system of North American cities.

As SAE Level-5 autonomous vehicles start to go into their testing phase, traditional car manufacturers are starting to seek potential in the ridesharing market. Take General Motors’ subsidiary firm Cruise for example, the company recently developed Origin, a line of electric shuttle vans that are specifically designed for ridesharing. Without any driver’s seat and steering wheel, the vehicle is expected to travel fully autonomously on designated city streets, making ridesharing much easier and comfortable. As autonomous ridesharing becomes increasingly convenient to use, vehicle occupancy rates in cities are expected to increase, reducing the burden of city roads.

AUTOCRYPT’s Role in Autonomous Driving

AUTOCRYPT is a total cybersecurity solutions provider for automobiles, providing all the security software components that are necessary (and soon mandatory) to keep autonomous vehicles safe on the road. With two decades of experience in authentication and data encryption technologies, AUTOCRYPT’s solutions ensure the legitimacy of all parties involved in V2X communications and the integrity of all data being transmitted. Recognized as the best automotive cybersecurity product/service by the prestigious TU-Automotive Awards, and one of the top 5 global market leaders for V2X cybersecurity by Markets and Markets, AUTOCRYPT is the foundation for the future of autonomous driving, ridesharing, and everything mobility.

Watch this video to see a brief introduction of AUTOCRYPT.

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Who Will Become the Next Tesla Challenger?

For the last two decades, the automotive industry has been focusing on disruptive technology and innovative productions. The advent of electric cars in early 2000 has provided more opportunities for new companies to challenge the industry – and Tesla has proven that it’s possible. While the existing manufacturing companies remained faithful to the internal combustion engine or by upgrading their models, Tesla has challenged the industry by producing its own all-electric car.

Run by Elon Musk, one of the world’s most powerful people, Tesla is now one of the top 10 most valuable American companies by market cap. Although the market paused for thought a few times, there’s no doubt that Tesla is becoming one of the most influential companies in the EV and autonomous driving industry.

According to Motor Trend, Tesla Model S beat Chevy, Toyota, and Cadillac for the ultimate car of the year honors in 2020 and according to tests, it has the lowest energy consumption of all BEVs. However, its cars are more expensive than traditional or hybrid cars (yes, the battery technology is very expensive!) and the company hasn’t been able to keep up with the high demands of production fueled by the green energy movement.

But it seems like Tesla isn’t the only startup disrupting the automotive industry after all. As hurdles are much lower than they’ve been in the past few decades, it’s been encouraging many electronics and IT giants to eye the EV industry with their innovative challenger electric cars. For instance, Sony has showcased their first Vision-S prototype, a full-fledged contempt car, in Las Vegas this January. Later on, Foxconn Technology Group and Fiat Chrysler Automobiles NV also discussed creating a JV to manufacture electric cars in China.

Lordstown Motors is developing a pickup truck called Endurance, intended for commercial fleets and it has been using some of the equipment that was used to make the Chevrolet Cruze. Lucid Motors’ CEO Peter Rawlinson was the Chief Engineer for Tesla before he left the company in 2012. Founded in 2007, their new car will have a range of more than 400 miles and be able to go from 0 to 60 miles per hour in under 2.5 seconds. Faraday Future is another LA-based startup that builds luxury sedans designed for autonomous driving. Canoo, another Californian startup, is planning to offer electric vehicles next year by subscription starting in LA and gradually expanding the service throughout the United States.

Then there are startups from China such as Byton, with a mid-size electric SUV that has a 48-inch horizontal screen running the entire width of the dashboard. NIO is based in Shanghai and is one of the few companies that are building and selling electric cars. Byton is another startup founded by a former BMW executive in China and this company is accelerating to begin volume production in Nanjing starting next year.

Another Chinese IT giant Huawei, on the other hand, is well known for its consumer electronics and information and communications technology (ICT) infrastructure, rather than electric car manufacturing, across the globe. But with the Chinese government and policy on its side, Huawei has started to scramble to march into the EV market. Since the Mobile World Congress 2018 event, Huawei launched its Intent-Driven Network (IDN) solution which helps evolve networks from SDNs towards autonomous driving networks. Moreover, they have also been developing autonomous driving networks in wireless network scenarios as well as aiming to simplify sites, architectures, along with protocols to build simplified networks.

Huawei has also been expanding its business and exploring autonomous driving networks and technologies with operators, proposing different levels of driving automation. They’re currently in the work of developing vehicles and infrastructure ends by rolling out products like the roadside unit, EI-based intelligent twins, and OceanConnect intelligent transportation platform. Although the company has announced that it won’t build cars but will just help automotive manufacturers like BYD to build better cars equipped with 5G that run on Huawei’s smart car system based on Harmony OS, the company’s investment in the automotive industry is still highly watched.

Almost Self-Driving Cars in 2020

Tesla stated that it will sell its self-driving computer chips and will be able to make its vehicles completely autonomous by the end of this year. Cadillac, Nissan, BMW, Mercedes-Benz, and Toyota are also making every effort to build fully autonomous cars. While the era of full self-driving cars hasn’t yet arrived, we should prioritize security technologies that make autonomous driving feasible without any external threats or interventions.

As experts predict there will be more than 125 million autonomous cars on the road by 2030, the actual concerns about autonomous driving-related to companies taking proper cybersecurity measures have been raised. Of course, protecting the vehicle itself is the utmost priority, but for now, companies are trying to build safer charging environments as the public has already seen attackers causing damage to electronic charging stations, which is the fundamental infrastructure needed to support EV operations.

AutoCrypt, Safeguarding the Fundamental Infrastructure

Many experts in the industry have been predicting the vehicles will become a rolling internet device, a smartphone on 4 wheels, which will become much more than just a means of transportation. Additionally, according to McKinsey, the automotive-related software market will double in value to USD 469 billion over the next 10 years. This means that your vehicle will not only become convenient, smart, and optimized but also a rolling data center, and securing the storage and data is the biggest legal challenge the EV manufacturers are currently facing.

Additionally, the current technology implemented at stations is mostly out-of-date open charge point protocol based on HTTPS which doesn’t encrypt data when safeguarding electric vehicle charging is the key to secure mobility. As driving an electric car is much more than just charging the battery, we need to make sure that our credentials and data are kept and exchanged safely through a reliable V2G security solution.

AutoCrypt V2G protects both the electric vehicle and its supply equipment (EVSE) during the PnC process, which uses PKI technology. The solution verifies the identities of both the vehicle and the charger, ensuring a safe exchange of information.

To learn more about AUTOCRYPT’s security solutions, click here.