IBM recently announced that its engineers have been granted a patent around a machine learning system that can dynamically shift control of an autonomous vehicle between a human driver and a vehicle control processor in the event of a potential emergency, providing a safety measure that can contribute to accident prevention. IBM was granted U.S. Patent #9,566,986: Controlling driving modes of self-driving vehicles for this invention.
IBM researchers reportedly developed the patented system by leveraging biological cognition and behavior generation in the brain. The developers’ backgrounds as computational neuroscientists led the inventors to devise a cognitive model and technique that employs sensors and artificial intelligence to dynamically determine potential safety concerns and control whether self-driving vehicles are operated autonomously or by relinquishing control to a human driver.
For example, the release outlines, if a self-driving vehicle experiences an operational anomaly like a faulty braking system, a burned out headlight, poor visibility or road conditions, a comparison may be made by the system as to whether the on-board self-driving vehicle control processor or a human driver is in a better position to handle the operational anomaly. If the comparison determines that the vehicle control processor is better able to handle the anomaly, the vehicle is placed in autonomous mode.
“Self-driving vehicles hold great promise and potential, but protecting the safety of passengers and other drivers remains a top priority for vehicle developers and manufacturers,” said James Kozloski, manager, Computational Neuroscience and Multiscale Brain Modeling, IBM Research and co-inventor on the patent. “We are focused on finding new ways to leverage our understanding of the human brain and inventing systems that can help those enterprises improve the safety of autonomous vehicles on the road.”
According to the IBM Institute for Business Value, automobiles are evolving from a mode of transport to a moving data center outfitted with sensors and computers that capture information about the vehicle, its driver, occupants and surroundings. At the same time, conversational interfaces are enabling drivers to interact with their vehicles more naturally and, with machine learning, automobiles can learn about their drivers and personalize the driving experience accordingly.
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