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However, autonomous vehicle technology is still learning how to master many of the basics – including recognising dark-skinned faces in the dark. In this sense, X-ray vision is an example of how an autonomous vehicle would improve upon the capability of a standard car operated by a human. In a real-life setting, CP would allow a moving vehicle to know that a pedestrian is about to walk out in front of traffic – perhaps because they're too busy looking at their phone – and brake in time to stop a collision.
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'This was achieved seconds before its local perception sensors or the driver could possibly see the same pedestrian around the corner, providing extra time for the driver or the navigation stack to react to this safety hazard,' said Professor Nebot. Right is a pedestrian 'about to make an error of judgement' by walking into the road. CP would allow a vehicle to brake in time to prevent a collision In one test, a vehicle equipped with the tech was able to track a pedestrian visually obstructed by a building. 'CP enables the smart vehicles to break the physical and practical limitations of onboard perception sensors.' 'This is a game changer for both human-operated and autonomous vehicles which we hope will substantially improve the efficiency and safety of road transportation,' said Professor Eduardo Nebot at the University of Sydney's Australian Centre for Field Robotics. The technology's applications are now being commercialised by Cohda, following the R&D work, which involved trials on public roads in Sydney. They've released their findings in a final report following three years of research and development.
The Australian project is being undertaken by iMOVE, a government-funded research centre, with support from transport software firm Cohda Wireless and the University of Sydney.
Therefore, teaching them how to respond to unique situations to the same capability as a human will be crucial to their full roll out. Autonomous vehicles are powered by artificial intelligence (AI) that's trained to detect pedestrians in order to know when to stop and avoid a collision.īut they can only be widely adopted once they can be trusted to drive more safely than human drivers.
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