Top 5 Self Driving Car Trends to Know in 2017

  • 14 June 2017 03:00:11 PM
  • By Kevin Rands

Self-driving cars are a future tech that have gotten a lot of hype and media attention with mixed opinions. On one hand, sure, self-driving cars could fundamentally change how we travel on a day to day basis, lower collision rates, and put a stop to rush hour traffic jams. Even Uber, the famous ridesharing service, has their own self-driving cars on the road in several cities.


That said, one of Uber’s self driving cars had a high-speed crash in March 2017 in Arizona and Tesla’s self-driving car was involved in an accident as well, although it was determined that the vehicle was not at fault.  


On the other hand, many people are concerned about the safety of autonomous vehicles, especially knowing that when accidents do occur with self-driving cars, they are likely to be much worse than those caused by human drivers, and the responsible party is not as clear cut. These issues especially raise a lot of concern for the insurance industry.


To redo our entire transportation infrastructure for these self-driving cars is a massive undertaking with a lot of kinks we’ve yet to work out. In fact, IEEE put together a list of 2,578 problems with self-driving cars.


Luckily, many of the best minds in the world have their eyes on these problems, and this is the direction they’re headed.


Here are the top self-driving car tech trends in 2017:

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1. Artificial intelligence

Artificial intelligence is a key technology for self-driving cars. AI as a whole is becoming increasingly advanced, and these steps forward are offering opportunities for safer, smarter cars as well.  


For example, Tesla’s cars use AI to determine when they should brake in a specific location based on the behavior of surrounding cars.


Alongside added processing power, more advanced AIs are able to make better decisions given the road data in front of them, which makes our roadways safer. Deep learning, especially, is a major factor for autonomous vehicles.


Using deep neural networks (DNNs), AIs in cars are able to understand complex information–a necessity for situations with a complex and ever-changing set of variables such as a road. As Peter Els at Robotics Trends explains, deep belief networks are being applied to several subsets of AI in order to make more effective self-driving vehicles. Among these are natural language processing and machine vision.

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2. Machine vision

Machine vision is important to self-driving cars for obvious reasons–it needs to see in order to avoid objects and obstructions. One current issue plaguing the self-driving car industry is the ability to accurately see objects in lighting conditions that vary, as can happen with rain, fog, etc. One proposed solution is the use of LIDAR, but there are concerns about both cost and the necessity for many cars to share the same frequency.


Machine vision is one subset of machine intelligence that will require advancements in order to make the most functional and self-autonomous vehicles, so many researchers are putting their energy toward this technology.

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3. GPU power


NVIDIA, the company who once dealt mainly in gaming graphics cards, is leading the way with GPU processing for autonomous vehicles. Their newest offering, the Xavier AI chip, a SoC AI supercomputer that saves energy while delivering massive power. Supporting it, the Xavier GPU engine has a whopping 512 cores.


GPU processing like this can support the types and level of machine vision and AI required to safely drive on roadways, is compact, and maintains energy efficiency. With technology like this, self-driving cars can react and learn at high speeds.

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4. CSEC for IoT


Cybersecurity for the Internet of Things is a major concern even outside of self-driving cars, especially with attacks such as Brickerbot and internet-stopping Mirai botnet occurring with seemingly increasing frequency.


While a hacked thermostat is a nuisance, a hacked car can be deadly.


Security researchers are hard at work looking for solutions for securing the IoT using a variety of methods, blockchain among them.


Making our cars less susceptible to malicious attacks will be a necessity for widespread adoption of autonomous vehicle technology to ensure not only continuous traffic flow but the safety of passengers.

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5. Electric power

Major self-driving cars are also using electric power vs. fossil fuels. This is no coincidence. Computers can more easily drive an electric-powered car and concerns about refueling lend to the idea that it would be safer for an autonomous vehicle to charge itself rather than attempt to fill itself with gas, according to Greg Gardner of USA Today.

Advancements in the efficiency of electric powered vehicles is one issue among thousands that has the attention of major researchers and investors in the automotive industry.


With self-driving cars expected to account for 95% of miles driven by 2030 according to RethinkX, we can expect to see even more innovation in this industry soon.


Have you used a self-driving car? Are you looking forward to the future of the industry? What’s your biggest concern? Let us know in the comments below!


Kevin Rands

Founder of Disruptor Daily. Serial Entrepreneur. Passionate about all-things disruption.



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