TAIPEI, Taiwan - Arm in collaboration with Taiwan Semiconductor Research Institute (TSRI) and STMicroelectronics announc...
TAIPEI, Taiwan - In order to assist station staff in solving problems with toilet complaints, Institute for Information ...
There will be adaption problems with the introduction of smart vehicles with autonomous driving or advanced ADAS to road systems in Taiwan or Southeast Asia. Taiwan has a traffic environment with a mixture of diverse types of vehicles, including cars, trucks, motorcycles, and bicycles, and this creates complex driving conditions on roads. Consequently, when faced with this mixed-vehicle traffic environment, the accurate identification of objects which are difficult of identify, such as people being tracked and two-wheeled vehicles, object trajectory tracking, and trajectory prediction entail high levels of technological difficulty.
Therefore, in Taiwan developing technologies for recognition, cognition, and warnings related to mixed traffic with diverse vehicles is extremely important. It will also assist in the development of safety support for the smart transportation industry and even aid the industry in selling to the international market.
MOEA, DOIT Director General Mr. Ta-sheng Lo stated that DOIT has invested in R&D resources for a number of years to assist domestic industries in the development of AI. Furthermore, at the beginning of September, they led industry, government, academic, and research circles to the United States for exchanges and cooperations with western academic circles.
On September 5, they also paid a special visit to Arm CSO Jason Zajac in San Jose, California in hopes that Arm can be integrated with Taiwan’s academic research for the R&D of AI technologies for Taiwan’s traffic environment with multiple types of vehicles and ICT support. This will enhance the self-driving and smart transportation industry value chain; moreover, it will also assist the ability of Taiwanese industry to play a key role in the international self-driving industry.
(TR. Phil Sweeney)
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