With government support and backing from tech giants, robotaxis are rapidly integrating into daily life in China in a push to position autonomous vehicles at the forefront of urban innovation.
But this rapid rollout has not been without controversy.
When the Robovan approached the stage at Tesla’s “We, Robot” event, it became evident that the electric vehicle maker was definitely not shying away from creating machines that look like they belong in a sci-fi movie. But while the event itself was thin on technical details about the Cybercab and the Robovan, CEO Elon Musk did share some information about the people hauler’s suspension system.
The Robovan looked like it was gliding on the pavement when it pulled up in front of the stage of the “We, Robot” event. The Robovan is very low on the ground, so much so that its wheels are almost not visible. This creates a very futuristic look, but it also brought concerns about the vehicle’s capability to traverse roads that are not perfectly paved. It also incited jokes from critics that the Robovan looks like a kitchen appliance.
In later comments on X, CEO Elon Musk highlighted that the Robovan is actually very airy inside even if it may appear otherwise from the outside. Musk also explained that the Robovan’s extremely low ground clearance is due to the vehicle’s automatic load-leveling suspension system. This allows the all-electric people-hauler to raise or lower its suspension depending on the conditions of the road.
Elon Musk on Thursday unveiled what he said was a robotaxi capable of self-driving, predicting it would be available by 2027—about a decade after he first promised an autonomous vehicle.
The Tesla CEO said the fully electric car—which has no steering wheel or pedals—would be priced under $30,000, would be charged wirelessly with inductive technology and would be “10 to 20 times safer” than human-driven cars.
“You can think of the car in an autonomous world as being like just a little lounge,” he told a crowd at the Warner Brothers Studio lot near Los Angeles.
“Our microwave induction heating technology enables fast and easy preparation of hard carbon, which I believe will contribute to the commercialization of sodium-ion batteries,” said Dr. Daeho Kim.
Can sodium-ion batteries be improved to exceed the efficiency and longevity of traditional lithium-ion batteries? This is what a recent study published in Chemical Engineering Journal hopes to address as a team of researchers from South Korea investigated how microwave induction heating can produce sufficient carbon anodes used in sodium-ion batteries. This study holds the potential to help researchers and engineers better understand how to develop and produce efficient sodium-ion batteries, which have demonstrated greater abundancy and stability.
“Due to recent electric vehicle fires, there has been growing interest in sodium-ion batteries that are safer and function well in colder conditions. However, the carbonization process for anodes has been a significant disadvantage in terms of energy efficiency and cost,” said Dr. Jong Hwan Park, who is from the Korea Electrotechnology Research Institute (KERI) and a co-author on the study.
Elon Musk revealed a prototype of the long-awaited robotaxi at a Hollywood film studio — but investors aren’t wowed.
Well it does kinda look like a stapler.
This wild-looking people mover can transport up to 20, Musk said.
Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience “catastrophic forgetting” when taught additional tasks: They can successfully learn the new assignments, but “forget” how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.
Biological brains, on the other hand, are remarkably flexible. Humans and animals can easily learn how to play a new game, for instance, without having to re-learn how to walk and talk.
Inspired by the flexibility of human and animal brains, Caltech researchers have now developed a new type of algorithm that enables neural networks to be continuously updated with new data that they are able to learn from without having to start from scratch. The algorithm, called a functionally invariant path (FIP) algorithm, has wide-ranging applications from improving recommendations on online stores to fine-tuning self-driving cars.
Be it water, light or sound: waves usually propagate in the same way forwards as in the backward direction. As a consequence, when we are speaking to someone standing some distance away from us, that person can hear us as well as we can hear them. This is useful when having a conversation, but in some technical applications one would prefer the waves to be able to travel only in one direction – for instance, in order to avoid unwanted reflections of light or microwaves.
For sound waves, ten years ago researchers succeeded in suppressing their propagation in the backward direction; however, this also attenuated the waves travelling forwards. A team of researchers at ETH Zurich led by Nicolas Noiray, professor for Combustion, Acoustics and Flow Physics, in collaboration with Romain Fleury at EPFL, has now developed a method for preventing sound waves from travelling backwards without deteriorating their propagation in the forward direction. In the future, this method, which has recently been published in the scientific journal external page Nature Communications, could also be applied to electromagnetic waves.
The basis of this one-way street for sound waves are self-oscillations, in which a dynamical system periodically repeats its behaviour. “I’ve actually spent a good part of my career preventing such phenomena”, says Noiray. Amongst other things, he studies how self-sustaining thermo-acoustic oscillations can arise from the interplay between sound waves and flames in the combustion chamber of an aircraft engine, which can lead to dangerous vibrations. In the worst case, these vibrations can destroy the engine.
Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience “catastrophic forgetting” when taught additional tasks: They can successfully learn the new assignments, but “forget” how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.
Biological brains, on the other hand, are remarkably flexible. Humans and animals can easily learn how to play a new game, for instance, without having to re-learn how to walk and talk.
Inspired by the flexibility of human and animal brains, Caltech researchers have now developed a new type of algorithm that enables neural networks to be continuously updated with new data that they are able to learn from without having to start from scratch. The algorithm, called a functionally invariant path (FIP) algorithm, has wide-ranging applications from improving recommendations on online stores to fine-tuning self-driving cars.
“Hyundai Motor Co., Korea’s largest car seller under Hyundai Motor Group, last month agreed on a strategic partnership with US auto giant General Motors Co. to jointly produce clean hydrogen in the US and develop future mobility solutions such as hydrogen fuel-cell cars and EVs.”
The leaders of Toyota Motor Corp. and Hyundai Motor Group will meet in South Korea later this month, raising expectations of a deepening partnership between the world’s No. 1 and No. 3 auto groups in future mobility technology.
Hyundai Motor announced on Tuesday that Akio Toyoda, chairman of Toyota Motor, will visit South Korea to attend the Hyundai N x TOYOTA GAZOO Racing Festival, which will be held at Everland Speedway on the grounds of the theme park in Yongin on Oct. 27.
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