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An Overview of Machine Vision and Autonomous Vehicles

machie vision autonomous vehiclesThere’s an obvious intrigue when it comes to autonomous vehicles because we all know, eventually, that they will be on the roads. It’s only a matter of time for the technology to advance to a point of full autonomy.

In the meantime, many questions remain. How safe can autonomous vehicles be? When will they hit the roads? What role will machine vision play in the safety and functionality of autonomous vehicles?

Current State of Autonomous Vehicles

There are five stages of autonomy when it comes to self-driving vehicles. Level 1 includes autonomous features such as cruise control, emergency breaking, or alerting a driver when they’re drifting lanes, but all level 1 features require the driver to still handle the majority of driving responsibility. Level 5 autonomy would be a fully autonomous vehicle, where the human driver rarely, if ever, needs to take the wheel.

Some of the most advanced self-driving vehicles in existence today are level 4s. This means they can drive fully autonomously, but only in pre-determined areas. For example, Google’s self-driving car is autonomous, but can only drive autonomously around Google’s campus. Similarly, the self-driving electric shuttles in Las Vegas, from a parternship between Navya and Keolis, are completely autonomous, but only in the new Innovation District designated by the city of Las Vegas. There are currently no level 5 autonomous vehicles on the road today.

Machine Vision’s Role in Reaching Level 5 Autonomy

Machine vision cameras and related technology will play an important role in not only the safety of autonomous vehicles, but in their ability to account for unexpected variables while driving - a key milestone for autonomous vehicles to achieve.

Determining the optimum Field of View (FOV) and Megapixels (MP) needed in a machine vision camera, for example, is a crucial consideration. As the FOV widens, more MPs are needed to see objects at a distance. For a 100o FOV camera, 8 MPs are needed to see a pedestrian at 67 meters, while a 45o FOV camera only needs 1 MP to see the same pedestrian at 67 meters.

Another important consideration is the frame rate, in frames per second (FPS), of the machine vision cameras. Frame rate is directly related to an autonomous vehicle’s stopping distance, which is a crucial safety feature. The difference in stopping distance between a 10 FPS camera and a 30 FPS camera, especially at high speeds, can be as high as 15 meters.

Frame rate, along with FOV and MPs, are a major part of what determines an autonomous vehicle’s ability to recognize and avoid obstacles, keeping the driver and pedestrians safe.

Machine vision capabilities will play an increasingly important role in the development of autonomous vehicles. Once machine vision can assist a vehicle in identifying and recognizing potential hazards, as well as how to avoid them, autonomous vehicles will be much closer to widespread deployment.

To get a deeper dive on this subject, watch our free archived webinar, "Improving Car Safety with Machine Vision – A New Perspective on Autonomous Vehicles."

 

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