Carlos Diaz-Ruiz, a doctoral student, drives the data collection car and demonstrates some of the data collection techniques the autonomous vehicle researchers use to create their algorithms
Autonomous vehicles that rely on artificial neural networks to navigate the world around them have no memory of the past, unlike humans, and are therefore in a constant state of seeing the world for the first time – no matter how many times they’ve driven down a particular road before.
This is particularly problematic in adverse weather conditions, when the car cannot safely rely on its sensors, say researchers at the Cornell Ann S. Bowers College of Computing and Information Science and the College of Engineering, who are currently researching how best to overcome this limitation by providing self-driving cars with the ability to create ‘memories’ of previous experiences and use them in future navigation.
Doctoral student Yurong You is lead author of ‘HINDSIGHT is 20/20: Leveraging Past Traversals to Aid 3D Perception,’ which You presented virtually in April at ICLR 2022, the International Conference on Learning Representations [‘Learning representations’ includes deep learning, a kind of machine learning].
“The fundamental question is, can we learn from repeated traversals?” said senior author Kilian Weinberger, professor of computer science at Cornell Bowers CIS. “For example, a car may mistake a weirdly shaped tree for a pedestrian the first time its laser scanner perceives it from a distance, but once it is close enough, the object category will become clear. So the second time you drive past the very same tree, even in fog or snow, you would hope that the car has now learned to recognize it correctly.”
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