Lifeboat Foundation

Designer Oscar Vinals introduced his concept for HSP Magnavem in 2018, but it’s getting attention again because of the developments in the industry.

In the Providentia++ project, researchers at the Technical University of Munich (TUM) have worked with industry partners to develop a technology to complement the vehicle perspective based on onboard sensor input with a bird’s-eye view of traffic conditions. This improves road safety, including for autonomous driving.

The expectations for autonomous driving are clear: “Cars have to travel safely not only at low speeds, but also in fast-moving traffic,” says Jörg Schrepfer, the head of Driving Advanced Research Germany at Valeo. For example, when objects fall off a truck, the “egocentric” perspective of a car will often be unable to detect the hazardous debris in time. “In these cases, it will be difficult to execute smooth evasive action,” says Schrepfer.

Researchers in the Providentia++ project have developed a system to transmit an additional view of the traffic situation into vehicles. “Using sensors on overhead sign bridges and masts, we have created a reliable, real-time digital twin of the traffic situation on our test route that functions around the clock,” says Prof. Alois Knoll, project lead manager TUM. “With this system, we can now complement the vehicle’s view with an external perspective—a bird’s-eye view—and incorporate the behavior of other road users into decisions.”

Move over, Elon Musk and Richard Branson: A Canadian company wants to join the fight for better high-speed train travel.

Toronto-based TransPod recently unveiled plans for a “FluxJet,” a fully-electric transportation system that’s “a hybrid between an aircraft and a train.” The project, currently in the conceptual stage, would involve 82-foot-long, magnetically levitated trains that would carry passengers at roughly 621 miles per hour.

Continue reading “This Canadian company wants to build a train-plane ‘hybrid’ that can go 620 miles per hour—take a look” »

UNSW engineers have built a new high-speed motor which has the potential to increase the range of electric vehicles.

The design of the prototype IPMSM type motor was inspired by the shape of the longest railroad bridge in South Korea and has achieved speeds of 100,000 revolutions per minute.

The maximum power and speed achieved by this novel motor have successfully exceeded and doubled the existing high-speed record of laminated IPMSMs (Interior Permanent Magnet Synchronous Motor), making it the world’s fastest IPMSM ever built with commercialized lamination materials.

Grass lawns need to be replaced.

The united states of America, is the 2nd highest CO₂ emitting country in the world and has the third largest population with approximately 330 million people.

Continue reading “How the suburbs are restoring biodiversity back to America” »

At this year’s Conference on Machine Learning and Systems (MLSys), we and our colleagues presented a new auto-scheduler called DietCode, which handles dynamic-shape workloads much more efficiently than its predecessors. Where existing auto-encoders have to optimize each possible shape individually, DietCode constructs a shape-generic search space that enables it to optimize all possible shapes simultaneously.

We tested our approach on a natural-language-processing (NLP) task that could take inputs ranging in size from 1 to 128 tokens. When we use a random sampling of input sizes that reflects a plausible real-world distribution, we speed up the optimization process almost sixfold relative to the best prior auto-scheduler. That speedup increases to more than 94-fold when we consider all possible shapes.

Despite being much faster, DietCode also improves the performance of the resulting code, by up to 70% relative to prior auto-schedulers and up to 19% relative to hand-optimized code in existing tensor operation libraries. It thus promises to speed up our customers’ dynamic-shaped machine learning workloads.

Researchers at Oxford University’s Department of Computer Science, in collaboration with colleagues from Bogazici University, Turkey, have developed a novel artificial intelligence (AI) system to enable autonomous vehicles (AVs) achieve safer and more reliable navigation capability, especially under adverse weather conditions and GPS-denied driving scenarios. The results have been published today in Nature Machine Intelligence.

Yasin Almalioglu, who completed the research as part of his DPhil in the Department of Computer Science, said, “The difficulty for AVs to achieve precise positioning during challenging adverse weather is a major reason why these have been limited to relatively small-scale trials up to now. For instance, weather such as rain or snow may cause an AV to detect itself in the wrong lane before a turn, or to stop too late at an intersection because of imprecise positioning.”

To overcome this problem, Almalioglu and his colleagues developed a novel, self-supervised deep learning model for ego-motion estimation, a crucial component of an AV’s driving system that estimates the car’s moving position relative to objects observed from the car itself. The model brought together richly-detailed information from visual sensors (which can be disrupted by adverse conditions) with data from weather-immune sources (such as radar), so that the benefits of each can be used under different weather conditions.

It will be commercially available in early 2023.

London-based SolarBotanic Trees (SBT) unveiled its solar and storage system that is shaped liked a tree. The company aims to deploy its technology to charge electric vehicles (EVs) to begin with, Electrek.

With the world moving towards less-carbon emissions, there is a rush toward harnessing renewable sources of energy. Not only do these technologies need improvements in their power generation efficiencies, but they also need to be aesthetically pleasing.