Tag: autonomousdriving

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.

To keep informed with the latest news on mobility tech and automotive cybersecurity, subscribe to AUTOCRYPT’s monthly newsletter.

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.

To learn more details about AUTOCRYPT’s solutions and services, click here.

To keep informed with the latest news on mobility tech and automotive cybersecurity, subscribe to our monthly newsletter.

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 Motor1.com 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 PnC protects both the electric vehicle and its supply equipment (EVSE) during the Plug&Charge (PnC) process, which uses PKI technology. The solution verifies the identities of both the vehicle and the charger, ensuring safe exchange of information.

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

It is no secret that the era of the autonomous vehicle is already here. With Tesla premiering the beta mode of their Full-Self Driving mode, and other manufacturers following suit with developments in autonomous technology, the number of connected and autonomous vehicles on the road will only continue to increase. However, that means that it is only a matter of time before the autonomous capabilities move on from the road to other methods of transportation. In fact, autonomous ships may not be very far behind from self-driving vehicles.

The International Maritime Organization (IMO) establishes the international standards when it comes to maritime traffic. The IMO defines ships that operate without human interaction as MASS, or Maritime Autonomous Surface Ships. They are also referred to as Unmanned Surface Vehicles (USVs), meaning that they are vehicles that travel on water, or smart ships, in the sense that they have capabilities to be able to travel on their own.

Although MASS or USV may be unfamiliar acronyms, autonomous ships and autonomous vehicles have more in common than you would think. Here are some commonalities between USVs and AVs.

Level / Degree Up

Just like a car has an autonomous driving level, decided by the SAE (see our blog post on different levels here), autonomous ships are also classified by levels of autonomy. However, the IMO officially defines the four levels (called “degrees”) from Degree one, where the system aids the seafarer’s decisions and navigation, all the way to Degree four where fully autonomous navigation occurs without seafarer or remote control.

Industry Consortiums

As a new(er) technology, autonomous vehicles have several organizations and projects that prioritize regulations and international standard compliance for testing, safety and continued development of the technology. It should therefore not come as a surprise that autonomous ships also have consortiums and researchers dedicated to continuing to define and develop the technologies. In 2016, a largely industry-led group called the Maritime Unmanned Navigation through Intelligence in Networks (MUNIN) published a detailed report which summarized three years of key findings regarding MASS.

Security First

As autonomous driving technology continues to advance and the deadline for WP.29 regulations approaches, a trending topic in the industry is security. For a car to drive autonomously on the road, it must connect in real-time to other vehicles, traffic lights, roadside units, and devices. If there is a vulnerability or a breach in this connectivity, true autonomous driving is not possible as it endangers the driver, passenger, and everyone around the vehicle. There is no reason why the same issue would not arise out at sea.

While there may not be a vessel right behind or next to a ship, sea vessels have other complex issues to figure out like weather conditions, route of nearby vessels, fuel capabilities, and load capacities to maintain. If there is a breach, there is the risk of danger to passengers, crew, as well as the sensitive products that may be in the middle of being transported. Hackers do not discriminate and will take a chance to infiltrate anything that seems of financial value or notoriety.

Why Autonomous Ships?

As the world becomes more interconnected, transport of goods will only increase. To optimize transport and minimize risk, it makes sense to develop autonomous ships – USVs can significantly reduce ship management costs as manpower and fuel account for over 80% of operational costs. Having unmanned autonomous vehicles will not only reduce costs but free up space. Minimizing amenities like food, water, and allowing additional cargo or fuel to be loaded will be groundbreaking.

Companies around the world have been taking notice. In 2018, Rolls-Royce and Finferries, a Finnish shipping company (state-owned), demonstrated the world’s first fully autonomous ferry in Turku, Finland. In South Korea, SK Telecom and Samsung developed an autonomous test ship. The 3.3-meter-long ship was equipped with 5G-based LiDAR, cloud-based IoT platform, as well as a real-time video monitoring solution. Korea’s government is also on board as the peninsula’s location is prime for maritime trade. The Ministry of Trade, Industry, and Energy as well as the Ministry of Maritime Affairs and Fisheries formed a working project for autonomous ships and is expected to invest over 160 billion won up to 2025.

However, as we have already seen with the rise of autonomous vehicles, another commonality is that new, trending technologies tend to become new and lucrative targets for hackers. Much like the WP.29 regulations by the UNECE, it may not be long before we begin to see similar regulations for other methods of transportation, and ship manufacturers and seafarers may need to begin preparation sooner rather than later. While technology continues to develop, roadmaps for regulatory reform and systems and standards for autonomous sailing personnel and cybersecurity.

It is essential to prioritize security from the beginning – that is one commonality of which we can be absolutely certain.

5G connects at speeds up to 100x faster than 4G, 100 gigabytes per second!

While there are many applications to 5G, autonomous vehicles (AVs) will most likely be a major part of how 5G is utilized because of its critical need for a low-latency, ultra-fast connection.

Here’s an overview of what 5G connections are and why AVs will make great use of this connection.
(Accessibility version below)

5G & Autonomous Vehicles

What exactly is 5G and how does it work for AVs?

5G refers to the fifth generation of wireless technology. 5G is expected to connect us to an ultra-fast, highly reliable network. With speeds of 100 Gbps, 100 times faster than its predecessor, 4G.

Where will we use 5G connections?

• Broadband and media
• Remote services
• IoT (Internet-of-things)
• Augmented reality (AR)
• and… autonomous driving

Autonomous vehicles (AVs) will be a major part of 5G application since low-latency, ultra-fast connections are crucial to safe operation of AVs. Self driving technology utilizes hundreds of sensors that will gather and exchange an enormous amount of data between multiple parties. This communication exchange is called V2X, Vehicle-to-Everything. V2X can refer to V2G (grid), V2I (infrastructure), V2P (pedestrian), V2D (device), V2V (vehicle), and also V2N (network).

Benefits of 5G-V2X or C-V2X can allow for:

• More precise automated driving, with response times as fast as, or possibly even quicker than, human behavior
• Better traffic control, less congestion
• High throughput for exchanging and processing raw data
• Support for larger numbers of simultaneous connections with low latency.

However, there are still challenges with deployment. Vehicle manufacturers want to see nationwide deployment before committing, but nationwide 5G coverage is still limited, which means vehicles cannot yet fully rely on the network. Service providers want to see more demand for 5G before providing deployment.

The future of 5G and C-V2X will depend on nationwide / global deployment and standardization across manufacturers, service providers and regulations.

• Building a stable 5G operator network with the solid reliability that autonomous vehicles require will likely take a few more years.
• 5GAA, a cross-industry organization for automotive tech, is working to define common standards to ensure that 5G for C-V2X meets the requirements for autonomous driving
• As carriers begin to roll out more 5G network support, manufacturers and service providers will likely begin to equip more vehicles with C-V2X.
• Cybersecurity providers will work to ensure that they can provide sophisticated end-to-end security to ensure that AVs with C-V2X will keep the safety of its drivers and passengers.

AUTOCRYPT is a member of 5GAA and provides end-to-end V2X and C-V2X security. Learn more about automotive cybersecurity on our webpage.

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Up until a few years ago, autonomous driving faced quite a bit of skepticism and was perceived by the public as a radical technology, far ahead of the times.

However, all of a sudden, cars actually started steering themselves. Public views quickly changed in 2017 when Tesla’s Enhanced Autopilot introduced mind-blowing features like traffic-aware cruise control, autosteer on divided highways, along with semi-autonomous navigation on certain roads.

Along with Tesla, many automakers started applying similar technologies to their vehicles. The speed of the rollout of new autonomous features quickly accelerated. As of 2020, most newly manufactured vehicles contain at least one autonomous feature, and industry experts expect autonomous vehicles to be available for mass consumption by the mid-2020s.

Therefore, over the next decade, we are likely going to see an increasing number of autonomous vehicles sharing our roads, though with a wide range in terms of the level of autonomy. This is because, despite how it may seem to the public, just because a vehicle may be manufactured with one or two autonomous features, does not mean that it is a fully autonomous vehicle. Depending on the level of autonomy, regulations on how the driver becomes  a part of the driving process may differ.

The Society of Automotive Engineers (SAE) utilizes a scale that defines six levels of vehicle autonomy, ranging from Level 0 (fully manual) to Level 5 (fully automated). This scale has been officially adopted by the US Department of Transportation, and is currently used universally by regulators and manufacturers worldwide as the de facto standard for autonomous vehicle grading. Here are the six levels of autonomous driving and where we are at currently.

Level 0 – No Automation

Today, around half of the vehicles (depending on where you live) on the road still belong to this category because most vehicles manufactured prior to the mid-2010s likely had no autonomous features.

Level 1 – Driver Assistance (2014~)

Key Aspects: Advanced Driver-Assistance Systems (ADAS) with Power Control or Steering

The two basic inputs that control a car’s movement are power and direction. Power is controlled by the accelerator and brake pedal, while direction is controlled by the steering wheel.

At the lowest Level of autonomy, Level 1, an autonomous vehicle is able to assist the driver in either power control or steering, but not both. Even if both systems exist, they work independently and do not communicate with each other.

This includes vehicles with adaptive cruise control (maintaining a safe distance from the car ahead) as well as lane keep assist and automatic lane centering. Still, such features are not meant to be relied on fully, as they are designed only to assist the driver, allowing them to use less force when steering or stepping on the brake or accelerator.

Market Situation. Around 2014, major manufacturers began adding features to their luxury models that would qualify them as Level 1 autonomous vehicles. Over the next few years, these features were gradually applied downwards to the economy models. Most cars made in 2020 are at least Level 1 on the scale of autonomy.

Level 2: Partial Automation (2017~)

Key Aspects: Advanced Driver-Assistance Systems (ADAS) with Power Control and Steering

Level 1 and Level 2 autonomy do not differ much in terms of technology. The only difference is that Level 2 vehicles have a more complete ADAS, enabling power control and autosteer simultaneously. Additionally, these systems can communicate with each other to ensure a smoother driving experience. For example, a car can slow itself down when the lane curves ahead.

Although a Level 2 autonomous vehicle can adaptively cruise on the road while keeping itself in lane, it cannot make lane changes or turns (unless the turn is guided by visible lanes on the ground). In theory, the driver could take their hands off the wheel and expect the car to drive without issues on highways. However, the car still requires the driver to keep their hands on the wheel and pay full attention to the road in case of unmanageable situations. Depending on the manufacturer, these cars sound alarms and disengage the autonomous features if the driver keeps their hands off the steering wheel for too long (usually between 10 to 20 seconds).

Market Situation. Tesla is one of the first manufacturers to bring Level 2 autonomous driving to the market. Starting in 2017, Tesla’s Enhanced Autopilot was slowly integrated into all their models. As of 2019, all Tesla models are equipped with Enhanced Autopilot. Other technologies competing with Tesla include Mercedes-Benz’s Drive Pilot, Cadillac’s Super Cruise, and Volvo’s Pilot Assist.

Level 3: Conditional Automation (2020~)

Key Aspects: Automated Driving System (ADS) with Environmental Detection

The jump from Level 2 to Level 3 is considered a significant breakthrough. So far, up to Level 2, the human driver takes primary control while the system provides secondary assistance. Starting at Level 3, the system acts as the primary driver while the human is only expected to override when it asks for assistance.

Level 3 autonomous vehicles are equipped with highly sophisticated sensors that are aware of complex traffic situations and environmental hazards, and the system is also able to respond to most situations. The vehicles use GPS mapping to self-navigate the road, and analyze the speed and distance of vehicles in neighboring lanes to safely make lane changes. Nevertheless, human override is still required in highly complex situations, such as congested traffic during rush hour.

Market Situation. In late 2018, Audi introduced the world’s first Level 3 autonomous car – the 2019 Audi A8, featuring Audi’s Traffic Jam Pilot system. Unfortunately, due to the current lack of cybersecurity standards for autonomous vehicles, US regulators demanded Audi to remove the Level 3 software components, downgrading it to a Level 2 vehicle for the US market. Apart from Audi, Tesla’s Autopilot also claims to have reached Level 3, along with the 2020 Mercedes-Benz S Class, followed by the 2021 BMW iNEXT.

Where We Are Now. The technology needed for Level 3 is indeed ready, but regulations and standards are lagging behind. Due to regulatory issues, we are currently stuck somewhere near Level 2.5.

As expected, when computer systems start to gain primary control of vehicles, cybersecurity concerns must be addressed. This is why AUTOCRYPT has been working closely with other members of the 5G Automotive Association (5GAA) in developing a set of international standards on automotive cybersecurity.

The good news is that the industry is very close to solving the problem. After the Road Vehicle Functional Safety Standard (ISO 26262) was established in 2018, the Road Vehicle Cybersecurity Engineering Standard (ISO 21434) is expected to be finalized by the end of 2020.

Japan and South Korea are some of the big markets that have recently approved the sale of Level 3 autonomous vehicles. South Korean automakers Hyundai and Kia are also finalizing their Level 3 technologies. By 2021, the market could expect a new wave of Level 3 vehicles.

Level 4 – High Automation (2023~)

Key Aspects: Automated Driving System (ADS) with Environmental Detection and Monitoring

Although we are somewhat stuck in terms of leveling-up, once Level 3 is deployed, Level 4 autonomy is not far from reality. Apart from enhanced sensors and environmental monitoring, these vehicles are supported by countless 5G connections that allow them to communicate in real-time with other vehicles on the road, with traffic lights, pedestrians, and so on – exactly why the V2X network is key to a highly automated road system. AUTOCRYPT, with its encryption and authentication technologies, provides a comprehensive solution to secure these connections, ensuring that drivers and vehicles are safe from hacking and data leaks.

Being highly autonomous means the vehicles are programmed to make logical decisions. Thus, Level 4 autonomous vehicles cannot perform acts considered to be dangerous driving, such as speeding or running traffic lights. If a driver wants to do these things , they would need to disengage the system in order to do so. Level 4 vehicles also require manual override under extreme weather and terrain, such as when there is low or no visibility, or when a driver wants to go offroad. However, these situations should be very rare.

Market Situation. Level 4 autonomous vehicles are expected to go on the road between the early and mid-2020s. However, rollout is more likely to begin with taxis and ride-sharing services, as it is likely to take a few more years for the mass consumer market to adapt to the change.

Level 5 – Full Automation (2025~)

Key Aspects: Automated Driving System (ADS) with No Human Driver

These are the very vehicles seen in Sci-Fi movies that many associate with autonomous vehicles: no steering wheels, operating fully on their own, and no driver – just passengers.

Market Situation. Level 5 autonomous vehicles are expected to be ready by the mid-2020s. Since there is no override option, a Level 5 vehicle cannot be a stand-alone product in itself. Similar to how a smartphone cannot be used without a cellular network, a constant 5G internet connection along with an intelligent transportation system is mandatory for Level 5 vehicles to function properly and safely. Therefore, these vehicles are expected to start out in restricted areas with smart infrastructures in place. For example, they are very likely to be deployed in certain areas for public transportation and ride-sharing, replacing taxis and buses.

Level 5 vehicles are unlikely to replace our personal cars because most people will still want the freedom to drive manually from time to time. Therefore, a Level 4 vehicle would be much more appealing to the mass market. At the end of the day, public roads will most likely host a mix of Level 4 and Level 5 vehicles.

AUTOCRYPT’s Role in Autonomous Driving

In the traditional IT industry, while it is necessary to implement security measures for network safety, it is not a prerequisite, meaning security software is not pre-embedded into the products. However, in the automotive industry, cybersecurity is absolutely a prerequisite because the negative consequences of a cyberattack could directly lead to the loss of lives.

This is why as we move into the era of Level 3 autonomy, we can expect an increased number of laws and regulations on automotive cybersecurity, ensuring that manufacturers have cybersecurity measures built into the vehicles.

AUTOCRYPT is currently working with a number of manufacturers and leaders in the automotive industry to not only provide a comprehensive automotive security solution, but also draft and implement global regulations and policies that will ensure that security is in place to keep all parties safe on the road.

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