Driverless vehicles in the chaos that passes for London’s streets are required to make 150 independent vehicle detections every second.
They can detect traffic lights in 1/2000 of a second, faster than the blink of a human eye, according to software provider Oxbotica.
The Oxford-based company is already using its pioneering Universal Autonomy software system in cities, mines, airports, quarries and ports.
This software can run on everyday computer hardware, similar to the power of an average desktop PC.
Oxbotica is also trialling five fully autonomous vehicles in London as part of the DRIVEN consortium, an £13.6 million research project that seeks to address fundamental real-world challenges facing self-driving vehicles — such as insurance, cyber-security and data privacy.
The company has completed extensive trials in London since initial trials in the Borough of Hounslow in Dec 2018.
Ranked as the sixth most congested city in the world, with more than 30,000 road casualties per year, the capital is proving the ideal testing ground due to its classical architecture and complex road networks.
The advanced journey learnings from London can then be applied to improve safety around the globe, whether that’s on the roads of Oxford or in a truck working a mine in Northern Australia – thanks to its machine learning algorithms and advanced vision perception.
Oxbotica founder, Paul Newman, said humans get better at driving with more experience, but we don’t share our learnings with each other.
“This is the covenant for autonomous vehicles,” he said.
“They learn as a community in a way that we don’t. If we, humans, have a mishap, or see something extraordinary, we aren’t guaranteed to make our neighbour or colleague a better driver.
“Even if we could learn from each other like computers can, we can’t share at scale, across vast numbers and we can’t do it all the time.
“That’s what our AI software will do for every host vehicle wherever it is in the world.
“Providing life-long shared learning, and with it in-depth, and continually improved knowledge of the local area – allowing our cars to not just read the roads but to predict common hazards with ever greater sophistication.”