Tag: autonomousvehicle

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.

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