Within the last years, driverless cars have emerged as one of the major workhorses in the field of artificial intelligence. Given the large number of traffic fatalities, the limited mobility of elderly and handicapped people as well as the increasing problem of traffic jams and congestion, self-driving cars promise a solution to one of our socities most important problems: the future of mobility. However, making a car drive on its own in largely unconstrained environments requires a set of algorithmic skills that rival human cognition, thus rendering the task very hard. This course we will cover the most dominant paradigms of self-driving cars: modular pipeline-based approaches as well as deep-learning based end-to-end driving techniques. Topics include camera, lidar and radar-based perception, localization, navigation, path planning, vehicle modeling/control, imitation learning and reinfocement learning. The tutorials will deepen the acquired knowledge through the implementation of several deep learning based approaches to perception and sensori-motor control in the context of autonomous driving. Towards this goal, we will build upon existing simulation environments and established deep learning frameworks.

Wayve, a U.K.-based self-driving startup that is notable for its use of deep learning and cameras rather than more-costly lidar and other sensors to guide vehicles, is gearing up for its next stage of development with a strategic backer in its pocket. Today Ocado — the online grocer that also powers online grocery systems for other retailers like Kroger in the U.S. — announced that it is investing £10 million ($13.6 million at today’s rates) in the startup.

By Bloomberg

BY DUNCAN RILEY

Artificial intelligence improves the accuracy of short-term weather forecasts and particularly the prediction of storms and heavy rain, according to research by the UK Meteorological Office and DeepMind, the London-based AI company. Their project focused on “nowcasting” — pinpointing the timing, location and intensity of precipitation at high resolution up to two hours ahead — which is not handled well by the supercomputer models used to forecast weather on a larger scale over the next day or week. This is critical for applications ranging from warning emergency services about an imminent flood risk to letting organisers of outdoor events know when a downpour is coming. The results, published in Nature, show that an AI-based approach called “deep generative modelling” or DGM outperformed other nowcasting methods over a wide range of measures. It was ranked first for accuracy and usefulness by 89 per cent of a panel of 56 professional meteorologists, who were blinded to the source of the predictions. “Improving the accuracy of short-term forecasting is an incredibly important endeavour,” said Niall Robinson, head of partnerships and product innovation at the Met Office, Britain’s national weather and climate service. “Extreme weather has catastrophic consequences including loss of life and, as the effects of climate change suggest, these types of events are set to become more common,” he added. “This research demonstrates the potential AI may offer as a powerful tool for improving our short-term forecasts and our understanding of how our weather patterns are evolving.” The DeepMind and Met Office researchers trained DGM to predict the development of precipitation (rain and snow) by analysing three years of UK radar maps. These show how much rain is falling every five minutes at 1km spatial resolution. AI analyses the past 20 minutes of radar observations and predicts rainfall for the next 90 minutes © DeepMind According to Shakir Mohamed, a senior DeepMind researcher, the AI technique involved is quite different from two other areas in which DeepMind has achieved success: playing games such as Go and determining the shape of protein molecules. The company was founded in 2010 and bought by Google in 2014. DGM focuses on the probability of sequences such as rainfall patterns playing out rather than on achieving a specific result such as winning a game or discovering how a protein folds. Robinson said the Met Office was considering how to use the DeepMind research in its operational forecasting. “We need to carefully consider how new tools are deployed and maintained, the best user interfaces for our meteorologists, and how it fits in with all the other forecasts we provide,” he said. Looking ahead, Robinson said the Met Office “is currently exploring where we might next collaborate on other research questions [with DeepMind], including applying AI to assess the impact of climate change and the processing of weather observations to create even better weather forecasts.” The researchers said the aim was not to supplant human meteorologists with automated experts but to improve their work. “AI could be a powerful tool, enabling forecasters to spend less time trawling through ever growing piles of prediction data and instead focus on better understanding the implications of their forecasts,” said Mohamed. “This will be integral for mitigating the adverse effects of climate change today, supporting adaptation to changing weather patterns and potentially saving lives.”