Up, up and away: Dubai hopes to have a passenger-carrying drone regularly buzzing through the skyline of this futuristic city-state in July. The arrival of the Chinese-made EHang 184 -- which already has had its flying debut over Dubai's iconic, sail-shaped Burj al-Arab skyscraper hotel -- comes as the Emirati city also has partnered with other cutting-edge technology companies, including Hyperloop One. The question is whether the egg-shaped, four-legged craft will really take off as a transportation alternative in this car-clogged city already home to the world's longest driverless metro line. Mattar al-Tayer, the head of Dubai's Roads & Transportation Agency, announced plans to have the craft regularly flying at the World Government Summit. Before his remarks on Monday, most treated the four-legged, eight-propeller craft as just another curiosity at an event that views itself as a desert Davos.
Transport ministers from the Group of Seven advanced economies agreed Sunday to strengthen cooperation in the railway and airline sectors as they wrapped up their three-day meeting in the resort town of Karuizawa, Nagano Prefecture. Prior to the conclusion of the gathering, ministers from Britain, Canada, France, Germany, Italy, Japan and the United States plus the European Union adopted a declaration Saturday pledging to reinforce international cooperation in creating safety regulations to promote self-driving cars. The conference was the last of the ministerial meetings related to May's G-7 leaders' Ise-Shima summit in Mie Prefecture. "We will cooperate with each other and exercise leadership to support the early commercialization of automated and connected vehicle technologies," the declaration adopted at the Saturday meeting said. "We obtained a common understanding to make efforts in the same direction to create regulation frameworks that (will) tend to vary depending on region," transport minister Keiichi Ishii told a news conference after the meeting.
SANTA CLARA, Calif., and JERUSALEM, Aug. 8, 2017 -- Intel Corporation (NASDAQ: INTC) and Mobileye N.V. (NYSE: MBLY) today announced the completion of Intel's tender offer for outstanding ordinary shares of Mobileye, a global leader in the development of computer vision and machine learning, data analysis, localization and mapping for advanced driver assistance systems and autonomous driving. The acquisition is expected to accelerate innovation for the automotive industry and positions Intel as a leading technology provider in the fast-growing market for highly and fully autonomous vehicles. The combination of Intel and Mobileye will allow Mobileye's leading computer vision expertise (the "eyes") to complement Intel's high-performance computing and connectivity expertise (the "brains") to create automated driving solutions from cloud to car. Intel estimates the vehicle systems, data and services market opportunity to be up to $70 billion by 2030. "With Mobileye, Intel emerges as a leader in creating the technology foundation that the automotive industry needs for an autonomous future," said Intel CEO Brian Krzanich.
China Abstract: Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications. To capture the complex non-stationary temporal dynamics and spatial dependency in multistep traffic-condition prediction, we propose a novel deep learning framework named attention graph convolutional sequence-to-sequence model (AGC-Seq2Seq). In the proposed deep learning framework, spatial and temporal dependencies are modeled through the Seq2Seq model and graph convolution network separately, and the attention mechanism along with a newly designed training method based on the Seq2Seq architecture is proposed to overcome the difficulty in multistep prediction and further capture the temporal heterogeneity of traffic pattern. We conduct numerical tests to compare AGC-Seq2Seq with other benchmark models using a real-world dataset. The results indicate that our model yields the best prediction performance in terms of various prediction error measures. Keywords: traffic forecasting; deep learning; attention mechanism; graph convolution; multistep prediction; sequence-to-sequence model 1. INTRODUCTION Automobile use has significantly increased in the past few decades owing to the steady development in both technology and economy. However, the increased automobile use has resulted in a series of social problems such as traffic congestion, traffic accidents, energy overconsumption, and carbon emissions (Gao et al., 2011). The intelligent transportation system (ITS) has been considered as a promising solution to improve transportation management and services (Qureshi and Abdullah, 2013; Lin et al., 2017).
"I don't want to be remembered as the guy that put a train in a tube" is the quote du jour from Hyperloop One's Josh Giegel. Giegel and co-founder Shervin Pishevar have been showing off a revised vision for how the future of public transportation will operate that moves far beyond intercity travel. But does this level of futurism run the risk of alienating governments and regulators who just want a cheap alternative to high-speed rail? We sat down with the pair to ask them to justify their even more utopian vision for the future of travel. The company's latest pitch video demonstrates this, showing how a route between Dubai and Abu Dhabi would work.