intellias-blog

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There are no clear signs that we’ll see driving automation above level 3 among the top automotive trends in 2019. No particular legal frameworks and technical requirements have been developed in the past year. Autonomous cars continue to be vulnerable to cyber attacks. And the driving environment still lacks smart traffic regulation and monitoring.

While major manufacturers including GM, Ford, Honda, Toyota, and Volvo claim that they plan to catch up with the latest technology trends in the automotive industry and launch self-driving cars next year, we won’t be surprised if they don’t. It’s more realistic to suggest that we’ll reach level 4 and 5 autonomy in passenger cars no sooner than the 2030s to 2040s.

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Since around 2010s, it may seem like there hasn’t been much new in the FinTech industry except for general digitalization, adoption of AI, and online payment integration. But this is just the tip of the iceberg, as new impressive technologies are about to hit the financial sector. Intellias is ready to bust the myth of the innate conservatism of the financial industry and introduce a devil’s dozen of innovative technologies that will rattle the field in 2019.

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This year, two major FinTech events took place at the same time in the opposite corners of the world. Money20/20 USA was in Las Vegas while Sibos was in Sydney. And because you can’t be at two different places at once, Intellias collected the most buzzing trends from both events, and now we are ready to share our findings. The conferences were attended by a total of 20 000 people. These lucky ones had the opportunity to listen to over 1 000 speakers. Read on to catch up on the hottest topics in the FinTech industry from these two events.

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Autonomous vehicles can get into many different situations on the road. If drivers are going to entrust their lives to self-driving cars, they need to be sure that these cars will be ready for the craziest of situations. What’s more, a car should react to these situations better than a human driver would. A car can’t be limited to handling a few basic scenarios. A car has to learn and adapt to the ever-changing behavior of other vehicles around it. Machine learning algorithms make autonomous vehicles capable of making decisions in real time. This increases safety and trust in autonomous cars.

Read an overview of the most popular machine learning algorithms for autonomous driving to find out what they do and why they matter for a fully driverless future.

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One of the biggest challenges the FinTech industry faces is attracting and retaining clients. Luckily, advanced technology can turn almost any soft spot into a business strength. This is the case with wearable technology and FinTech. Wearables in the financial sector help stakeholders learn user preferences, fascinate users with new services, and provide unparalleled experiences.

The future of wearables in FinTech depends mostly on the evolution of the Internet of Things in this industry. That’s no surprise, since wearables are part of the broader IoT trend and can’t be treated as a standalone phenomenon. Let’s see how wearables and IoT technology help FinTech players stand out.

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How to Realize OTA Updates for Connected Cars For the autonomous driving ecosystem to thrive, OTA updates will play an essential role. Along with V2X connectivity, OTA updates allow the cars ahead to share detailed information immediately for cars behind, to improve traffic flow. The cycle of content updates from infrastructure, vehicles and traffic systems, must be reduced from months or years to seconds for this future to be realized.

Essential components of OTA content updates will include data on:

Road repairs

Weather conditions

Closed roads

Alternative routes

Traffic jams

Locations of e-vehicle charging stations

Points of interest

[intellias-blog]

For the autonomous driving ecosystem to thrive, OTA updates will play an essential role. Along with V2X connectivity, OTA updates allow the cars ahead to share detailed information immediately for cars behind, to improve traffic flow. The cycle of content updates from infrastructure, vehicles and traffic systems, must be reduced from months or years to seconds for this future to be realized.

Essential components of OTA content updates will include data on:

Road repairs

Weather conditions

Closed roads

Alternative routes

Traffic jams

Locations of e-vehicle charging stations

Points of interest

Let’s find out how to implement one of the most essential OTA updates that are already in use, namely map updates for connected car navigation.

[intellias-blog]

Three Ways of Testing ADAS in Autonomous Cars Beyond a Test Drive - ADAS development process using V-Model

Vehicle-in-the-loop (VIL)

With the VIL method, a real autonomous vehicle and a human driver inside it operate in a simulated environment. The vehicle drives in virtual traffic either by itself or controlled by the driver when needed. The vehicle-in-the-loop method is useful for studying human behavior inside an autonomous car. For instance, it’s good at evaluating warning systems and how people react to them.

This is how different X-in-the-loop approaches to autonomous vehicle testing correspond to the different levels of the ADAS development process.

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Three Ways of Testing ADAS in Autonomous Cars Beyond a Test Drive - Vehicle-hardware-in-the-loop (VEHIL)

VEHIL is a multi-agent simulation. This means that, besides a real autonomous vehicle, several other artificial robotic platforms are in the lab. By using the VEHIL method, you can test a vehicle’s performance with targets that simulate other vehicles on the road. So yes, there is a way that you can actually test collision avoidance and adaptive cruise control. Here’s how the VEHIL closed loop works. 

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Three Ways of Testing ADAS in Autonomous Cars Beyond a Test Drive -  ADAS prototyping with a virtual environment using the SiVIC platform 

Creating a virtual environment for ADAS testing means modeling a complete driving scenario using software. This includes the driver, sensors, traffic, and realistic vehicle dynamics. In contrast with real-world testing, virtual environment simulation is safe. Also, it allows testing self-driving cars in various scenarios. A virtual environment helps to validate many aspects of vehicles at a time, decreasing development costs where possible.

Moreover, virtual environments for ADAS testing help to prototype and develop new system features. They help researchers create more reliable ADAS and integrate different advanced driver assistance systems to develop better autonomous driving technology.

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Advanced driver assistance systems (ADAS) are the first step toward a fully automated future. Features like emergency braking and adaptive cruise control already help drivers on the road and reduce the risk of error. But the performance of these systems isn’t always perfect. That’s why before hitting the road, advanced driver assistance systems in autonomous cars have to go through different testing processes to prove their safety.

Let’s take a closer look at how to test a self-driving car and the most common ways for testing advanced driver assistance systems.

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Have you ever wondered why autonomous vehicles need so many sensors? Driverless cars carry sensors on the roof, windshield, bumpers, and even side mirrors.

Sensors are the eyes of an autonomous car. They allow it to see the world and safely operate within it. But with so many different types of sensors on the market, it’s hard to choose the right ones. On top of that, relying on just one kind of sensor would be shortsighted because each has its strengths and flaws.

Intellias has written an overview of lidar and radar — two of the most popular sensors for autonomous driving technology. Let’s compare their main features and find out which works best with driverless vehicles.

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Imagine an autonomous car driving down a busy city street. Suddenly, it spots children crossing the road. There’s oncoming traffic to the left, a building to the right and a truck driving just behind.

What should the autonomous vehicle do? Keep going and hit the kids? Or save them by turning into a wall, even if that means hurting the passengers? This mental exercise illustrates the ethical dilemma of self-driving cars. How should they behave in unavoidable crash scenarios? Whom should they save first? And who will be responsible for the damage?

Intellias has put together an overview of autonomous vehicle ethics. Read on to find out how a self-driving car learns ethical principles and what the ethical issues of driverless cars are.

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Why Wired is Wrong about the Future of Transportation

V2X connectivity.  The progress of V2X technology

By communicating with other vehicles, infrastructure, and real-time cloud data, autonomous vehicles adapt to their surroundings to ensure higher speeds, less time sitting in traffic and a remarkable reduction in road fatalities.

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Riding a bike to work can be a good idea. But the reality is a lot different when you consider the extra time lost for the journey distance alone. Not to mention arriving to work dripping with sweat. Your appearance disheveled to say the least, and missing documents from the bag you’ve been carrying while trying to stay upright.

While you concentrate on your surroundings to navigate your bike through crowds, traffic and pedestrians, you’re left with little time to battle the weather, time constraints and even think about the day ahead.

Aside from the practicalities mentioned, is there any wonder why our generation has been striving to advance the automotive industry for decades?

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Road incidents that involve even the mere possibility of a self-driving vehicle hitting a pedestrian scare away consumers. Recent tragic events like an Uber car killing a woman have motivated OEMs to search for new means to ensure safety.

According to a report by Deloitte, users care more about their safety than about all the cool features autonomous cars have to offer. It looks like now is the right time to stop and rethink the very concept of self-driving cars, focusing on safe driving before cars even hit the road.

Intellias has written an overview of the most promising features that can help make self-driving cars safe in the eyes of consumers.

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Let’s Make Self-Driving Cars Safe Again Blockchain technology and SDP perfectly complement each other to increase the cybersecurity of self-driving cars. The blockchain allows secure messaging by encrypting data and a software-defined perimeter protects the vehicle’s communications with cloud applications.