An empty street, a row of parked cars at the side: nothing to indicate that you should be careful. But wait: Isn't there a side street up ahead, half covered by the parked cars? Maybe I better take my foot off the gas -- who knows if someone's coming from the side. We constantly encounter situations like these when driving. Interpreting them correctly and drawing the right conclusions requires a lot of experience. In contrast, self-driving cars sometimes behave like a learner driver in his first lesson.

In this week's #DataTalk, we talked about autonomous vehicles and computer vision with Dr. Inmar Givoni, who is the Autonomy Engineering Manager at Uber Advanced Technology Group. Prior to that she was the Director of Machine Learning at Kindred, where her team developed algorithms for machine intelligence, at the intersection of robotics and AI. She was the VP of Big Data at Kobo, where she led her team in applying machine learning and big data techniques to drive e-commerce, customer satisfaction, CRM, and personalization in the e-pubs and e-readers business. She first joined Kobo in 2013 as a senior research scientist working on content analysis, website optimization, and reading modeling among other things. Prior to that, Inmar was a member of technical staff at Altera (now Intel) where she worked on optimization algorithms for cutting-edge programmable logic devices.

Identifying lanes on the road is a common task performed by all human drivers to ensure their vehicles are within lane constraints when driving, so as to make sure traffic is smooth and minimise chances of collisions with other cars in nearby lanes. Similarly, it is a critical task for an autonomous vehicle to perform. It turns out that recognising lane markings on roads is possible using well known computer vision techniques. We will cover how to use various techniques to identify and draw the inside of a lane, compute lane curvature, and even estimate the vehicle's position relative to the center of the lane. The first step we will take is to find the calibration matrix, along with distortion coefficients for the camera that was used to take pictures of the road.