Despite the giant leaps in artificial intelligence (AI) algorithms trained on data gathered from sensors both inside and outside the car, the seemingly rapid advances to date may only prove to be the low hanging fruit. Cracking the last mile will be harder and take a lot longer.
"It is the last 10% of cases and situations that is proving a bottleneck in development," Matthew Avery, Director of Research at motor insurance industry funded researcher Thatcham Research was quoted as saying in the Guardian.
The bulk of rules for AVs such as following the line of the road, sticking to a certain side and avoiding crashing into other cars can be addressed by AI algorithms.
But it is far more difficult for algorithms to address what Avery refers to as "edge cases" - rare and unusual events that a self-driving vehicle has not encountered before. Examples might be a dog running into the road or an unexpected weather-related accident, for instance.
Cracking the last mile
There are five grades of automated vehicle systems as classified by the US-based Society of Automotive Engineers (SAE). These range from functions which automate distance control to totally autonomous vehicles, which means there is no requirement for a driver even to be present behind the wheel. Level 5 AVs may even lack a steering wheel as well as accelerator and brake pedals. Passengers might use voice commands to select a location or control what TV show they want to watch in transit. Crucially, level 5 vehicles are meant to be able to operate on roads anywhere, not just in certain designated areas.
Professor Michael Felsberg, Head of Sweden's Link?ping University's computer vision lab says several problems stand in the way of achieving such a goal. One of them is image classification. "We know that for each image, this is a bicycle, this is a dog and this is a car," he explains. "The images are hand-labelled by humans and the annotated images are used to train image recognition systems."
Felsberg explains that AI algorithms require a period of supervised learning before a system can be deployed. In preparation for this phase, an army of annotators is needed to label the images for a given application. Images are annotated with not only the name of the class of objects the algorithm should look for, but also the location of the object within the image.
For large-scale industrial use of AI, this amount of annotation is impractical, Felsberg says. "For autonomous vehicles to work on a large scale, algorithms should be able to recognize new classes of objects without having to undergo another round of supervised training. It takes too much time and effort to re-label the huge volumes of data. It would be much better if the algorithm could learn to recognize the new class after it has been deployed."
Researchers have yet to come up with a robust and effective method for this process, which is referred to as "class incremental learning".
What the IEC can do
Self-driving vehicles use sensors, cameras, radars and in some cases laser imaging, detection and ranging (LIDAR) technology to gather the data required to run autonomously. Several standards have been developed that can help with autonomous transport. IEC TC 47 publishes IEC 62969, which specifies the general requirements of power interfaces for automotive vehicle sensors.
IEC TC 100 issues several standards relating to multimedia systems in cars. One of its publications is IEC technical specification (TS) 63033,which specifies a model for generating the surrounding visual image of the drive monitoring system, which creates a composite 360 degree image from external cameras. This enables the correct positioning of a vehicle in relation to its surroundings, using input from a rear-view monitor for parking assistance as well as blind corner and bird's eye monitors.
(Source: IEC)
