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What’s Next for Self-Driving Cars?

When it comes to technology in cars, people talk about one application more than anything else. Self-driving cars have been a hot topic of conversation for years, but the technology doesn’t seem any closer. Where is the autonomous vehicle development now, and where can it go from here?

Transportation authorities divide vehicle autonomy into six tiers, which are as follows:

  • Level 0: No automation
  • Level 1: Assistive features like adaptive cruise control are present, but drivers still control the car’s core functions.
  • Level 2: The vehicle can use different assistive technologies at the same time.
  • Level 3: The car can navigate itself under certain conditions, but a driver has to be ready to take control at any time.
  • Level 4: Under some circumstances, the vehicle can drive by itself without needing any human supervision.
  • Level 5: The car can do all the driving in all conditions.

Level one and two cars are widespread today, but anything higher than that is rare. You could consider some functions, like Tesla’s autopilot, level three, but those are still few and far between. So how can manufacturers move forward with this technology?

Speedbumps in autonomous car development

The self-driving car industry has been stuck at level three for some time now. General Motors had more than 40 level-3 vehicles in testing in 2016, but not much has changed. As these tests have continued, developers have noticed some roadblocks between levels three and four.

GM-CruiseAV-RoofRack-General-Motors-Self-Driving-Cars-Autonomous-Vehicles-The-Boss-Road-Test
General Motors test vehicle

The machine vision systems in cars today are excellent at recognizing obstacles like other vehicles and pedestrians. Anticipating how they’ll act is another issue entirely. People behave irrationally by running red lights or jaywalking, and that kind of behavior is hard for an AI to react to or expect.

Related story: When Self-Driving Cars Decide Who Lives and Who Dies

These AI systems will get better with more training data, but collecting that data can be complicated. Right now, putting an autonomous car on the road can be dangerous, but they need to be out there to gather data. As a result, the process of getting all the necessary training may be a long one.

So, where can self-driving cars go from here? Here are a few possible next steps.

Public transportation

Autonomous cars may not be ready to disrupt the industry, but implementation is still possible. Public transportation is an ideal application for today’s self-driving vehicles because it’s a more predictable form of driving. By driving pre-defined routes at slower speeds, autonomous public transports can start to gather that all-important training data.

Some companies have already started taking advantage of this area. A business called May Mobility has been running self-driving shuttles to train stops since May 2019. Since these routes are short, slow, and straightforward, they’re the perfect real-world training ground.

As these technologies start to get better, cities can apply them to vehicles on busier roads, like buses. It may be a slow, methodical approach to implementation, but it gives self-driving cars real-world experience.

Ridesharing

Ridesharing is the private sector’s answer to public transportation and is also ideal for driverless cars. Uber’s self-driving division already accounts for 10% of the company’s value, or more than $7 billion. On top of being profitable, it’s a logical next step for developing driverless cars.

The routes these services take are typically short, so it’s easier to get from one point to the other safely. These vehicles would also have to navigate urban traffic, which provides essential data for future cars. With more of these vehicles on the road, other drivers would also get used to driving alongside them.

Ridesharing companies shouldn’t transition to autonomy all at once, but a gradual approach will be helpful. As they start to implement more driverless cars, all parties will benefit.

Smart cities

The answer to safer driverless cars may lie outside the vehicles themselves. The more data points a car’s AI can connect to, the better picture it will have of the road. As a result, smart cities, with widespread IoT and 5G infrastructure, may be the key to vehicle autonomy.

Toyota Smart City Concept Image
Toyota concept image of a smart city

Toyota has started to implement this idea in training to see how it will play out. The company announced plans to build a 2,000-person smart city at this year’s Consumer Electronics Show (CES). If this town of the future proves promising, Toyota may establish a precedent for the infrastructure of tomorrow.

Video: Umbrellium Concept for Responsive Road Smart Cities

As cities implement more IoT infrastructure, it will improve and expand edge computing. Driverless cars can use this to navigate the streets better. The result will be safer roads for all drivers and passengers.

Autonomy is inevitable, but it isn’t near

The automotive industry is still far from achieving level five autonomy. It could take another ten years or so. That said, current technology is promising, even if it hasn’t seemed to advance much in the past few years. It will take a while to move from level three to level four, but it will happen.

With slow, careful implementation, driverless car companies can gather the necessary data. Wider technology adoption like smart cities will help drive the concept forward too. Even if it may take longer than expected, the future of self-driving vehicles is inevitable.

This guest article has been prepared by Martin Bank. Thanks, Martin, for the contribution!

Photo credit: The feature image and the self-driving car photo are owned by GM and has been provided as part of a press kit. The smart city concept image is owned by Toyota and has been provided as part of a press kit.
Source: National Highway Traffic Safety Administration, U.S. Department for Transportation / GM press release / Neal E. Boudette (New York Times) / Sameepa Shetty (CNBC) / Oscar Holland (CNN)

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