Created: Wednesday, 14 February 2018
Written by William Gray
Shanghai, Friday, rain expected. Another rainy day in the city, leaving for work by public transport is quite popular, and carrying an umbrella for the regular days of rain the norm. However, all this might be about to change, and before you begin thinking of a conspiracy to change the weather, it’s the smaller item that is changing.
With advances in technology, and decrease in cost of components, there are now 10,000 umbrellas on the streets of Shanghai, each can be rented instantly. Some may remember when this happened before at the beginning of 2017, that project didn’t get off to such a great start, losing 300,000 umbrellas. Justin Jia, Zhejiang Tianwei Umbrellas, has taken up the challenge, the Chinese entrepreneur has created a pioneering sharing app for umbrellas, and currently you can rent one of the 10,000 umbrella’s in the city.
Using both a deposit, and then scoring on the person’s behaviour, eg. reporting , damaging, returning on time, etc. the data is key to the profitability. Rather than monitoring the umbrella, it is more the person that is being monitored. Much like you may improve your credit rating, soon you’ll be able to improve your share-ability rating.
So next time it is a rainy day, you soon may be able to leave your umbrella at home.
Created: Friday, 09 February 2018
Written by William Gray
Apologies to the younger people in the audience, but the image for today’s article might be classed as what your iPad is descended from, it’s a BBC Micro (Short for Microcomputer) if anyone asks. The relevance being that what is under the bonnet of a self driving car today, will in a few years time seem like the BBC Micro’s of the self driving world. Currently developers and technology architects are struggling with the cost, reliability, and cooling of the chips and computational power that they are having to put under the bonnet to create relatively safe and secure environments.
Over at Huawei Research Canada, the team have just released what could be a significant step forward in image prediction. There have been software models available (CDNA) that by using over 12 million points, have been able to give a good representation of what the next image would look like. In a sense this is predicting the future image on your dashcam. The processing power to deal with that amount of data as you can imagine is costly, and runs hot.
Thanks to work by the team at HRC ( Donglai Zhu∗, Hao Chen∗, Hengshuai Yao, Masoud Nosrati, Peyman Yadmellat, Yunfei Zhang ) (Practical Issues of Action-conditioned Next Image Prediction 9-Feb-2018) using a smaller tiling set of under 1 million points with a single-decoder feedforward architecture manages to give a comparable performance on image prediction.
Predicting images, ie. how the world around a car will look in the (very near) future is essential for matching how humans predict situations when driving. Eg. if you notice a jogger running towards the road on a trajectory that will cross the road, it allows you time to break should the jogger not stop. With automated car driving having predictive imaging, this same action of slowing can take place, ensuring other human drivers around the automated car will not be disturbed by sudden breaking when the jogger ends up running across the road.
The research by the HRC team is excellent, bringing together of 2 technologies to give lighter faster design, which will enable cheaper, more energy efficient vehicles in the future.