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.

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

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.

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.

The Rise of Autonomous Delivery Vehicles

The Rise of Autonomous Delivery Vehicles

Autonomous vehicles are growing in number, and many drivers dream of a future where they can simply be passengers on a commute, a road trip, or just a leisurely drive. Technology has been quickly developing, but what many may not be aware of is the quick, or possibly quicker, rate at which autonomous trucks are hitting the roads.

Let’s take a look at some of the trending topics when it comes to autonomous trucks.

Pandemic Trends

It’s no secret that many of us have been sheltering in place and locking down due to the novel coronavirus, COVID-19. While social distancing, many people have been buying household goods and ordering food online for delivery, and even buying groceries through apps like Instacart. The demand for stores to move to e-commerce has seen a surge, and while this may seem like good news for business owners who may be having a difficult time with drops in offline consumers, it can still have some drawbacks. In fact, the demand may be too high for companies and laborers to keep up.

In Korea, a major online-shopping hub, statistics showed that online shopping amounted to USD 10.6 billion in May 2013, a 13.1 percent increase from the previous year. Food delivery rose 77.6 percent, and F&B online purchases went up 33.1 percent. The steep increase has led to some traumatic instances, with 14 delivery workers losing their lives in 2020.

(Credit: Nuro)


Many companies, therefore, have moved to utilize autonomous deliveries, either with drones, carts or in some cases trucks. Companies have been gaining investor interest.

A great example is Nuro, a California-based autonomous delivery vehicle company, who have been delivering goods autonomously since 2018. Nuro has received the first and only federal exemption for AVs by the US Department of Transportation as well as the National Highway Traffic Safety Administration, and is currently operating in California, Texas and Arizona. The need for a socially distanced method of ecommerce does seem to be gaining traction.

Autonomous and Electric

Autonomous delivery vehicles like pods or trucks are a perfect use case for autonomous-driving technology, as often truck drivers have to go long distances. However, trucks have a much higher rate of carbon emissions because of the sheer size of vehicle and distance of driving. It’s estimated that heavy duty vehicles (HDV) are responsible for 30% of all transport emissions, despite the fact that they only make up roughly 5% of vehicles.

Therefore, it makes sense that the next stop would be to make them all electric, moving away from the traditional diesel-fueled trucks. Companies like Swedish startup Einride have jumped on the wagon, as they recently unveiled a new line of electric, driverless (with remote operator) freight trucks. The company expects to have them on the road in 2021, with emissions cut by up to 90% and fuel costs reduced by 70%.

The challenge for this new development of the electric ad autonomous vehicles will be range anxiety, as Battery Electric Vehicles (BEVs) often have a specific limit of driving range before needing to be charged. BEVs that are also HDVs often have mileage ranges of about 80-110 miles. This range may work for European nations, where distances are shorter and the number of charge points is greater. However, long-haul, cross-country drives within the United States may prove to be more of a feat. A more favorable starting point would be implementing city-wide delivery programs, where charging points are more densely available.

Security Concerns

Of course, when it comes to any new technology, we prioritize security. When looking at autonomous delivery vehicles, in theory they need to implement the same cybersecurity management systems and protocols with a lighter vehicle.


ECUs within the car as well as the network that they are connected to need security measures to be put in place, in order to secure communications and messages that are essential to the proper operation of the delivery vehicle.

Additionally, with trucks and larger HDVs, greater attention needs to be paid to sensors, as they have larger blind spots than lighter vehicles. With side-view assist and expanded LiDAR, trucks can reduce this risk. In 2010, the Insurance Institute for Highway Safety (IIHS) found that with side-view assist, up to 39,000 crashes could be prevented each year.

(Credit: DOT Share the Road Safely)

Conclusion

While autonomous driving technology has been progressing relatively quickly, it is nowhere near Level 5 Full Automation where it can be truly driverless. In fact, many autonomous delivery services still ensure that a driver and an engineer are always on standby to take over manually if the need arises.

But this is not to say we will not be seeing full autonomous trucks anytime soon. It is important to keep in mind that though passenger vehicles are featured in the headlines, smart mobility expands into many other types of transportation – along with cars, trucks and pods are also in development and are deploying out onto public roads. Autonomous trucks will likely be one of the first wide-scale use cases because of its benefits, but we have been seeing a surge in robotic carts that are able to truly make social distanced deliveries happen.

However, as always with new technology, securing them and their environments is a large component of large-scale adoption, if not the most important component. As regulations like WP.29 continue to change the automotive industry, it will be interesting to see how delivery services fit into the larger picture of automotive cybersecurity.

To learn more about AUTOCRYPT’s autonomous vehicle security solutions, check out our page here.

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 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 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.

Autonomous Vehicles… and Ships?

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.