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Raquel Urtasun. First International Workshop on Computer Vision for Autonomous Driving. Call for Papers | Submission | Committees | Invited Talks | Best Paper Award | Program US Lawmakers have recently passed legislation that allows fully autonomous vehicles to share public roads. With their potential to revolutionize the transport experience — and to improve road safety and traffic efficiency — there is a strong push by vehicle manufacturers and government agencies to bring autonomous to the broad market.

The recent demonstrations at the DARPA Grand Challenges and by industry leaders has established that the core technical barrier to achieving autonomous vehicles is road scene understanding. However, although vehicle infrastructure, signage, and rules of the road have been designed to be interpreted fully by visual inspection, the use of computer vision in current autonomous vehicles is minimal. Are current methods and representations adequate to hand over the wheel to computer vision algorithms? Call for Papers (pdf) A paper ID will be allocated to you during submission.

Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization. Andreas Geiger. Complete Car | Test Drives: BMW Autonomous 5 Series prototype. We 'drive' BMW's autonomous 5 Series prototype on the public road. When: August 2011 Where: Munich, Germany What: Autonomous BMW 5 Series prototype Occasion: First ever public road trial Ever since the likes of Herbie, KITT and the Batmobile graced our screens we've been enthralled by the idea of a car that can drive by itself.

In fact, it's built two: a pair of 5 Series decked out with 12 exterior sensors that read the road around the car. When the first prototypes were ready, testing began on race circuits and the project became known as TrackTrainer. The technology allows the car to travel with near pinpoint accuracy and it can now be used on the road. Once you've reached a steady motorway cruise, prod what would normally be the steering wheel's volume button a couple of times and the car takes over. The first few minutes are incredibly eerie.

It hugs the inside lane by default, but it's not afraid to overtake slow moving traffic. The technology isn't perfect, though. Learning From Data - Online Course (MOOC) A real Caltech course, not a watered-down version on YouTube & iTunes Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data.

What is learning? Live Lectures This course was broadcast live from the lecture hall at Caltech in April and May 2012. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible? Machine learning textbook. Introduction to Statistics.

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Deep Learning. Books. Autonomous vehicles.