Taipei, Wednesday, Jun 26, 2019, 20:42


NARLabs and Acer Collarorate on Development of Self-Driving Automobiles

By Korbin Lan
Published: Dec 18,2018

TAIPEI, Taiwan - NARLabs and Acer Incorporated have signed a letter of intent of cooperation for testing verification of Acer's smart automobiles, which will be carried out at the Shalun Smart Energy Green Science City (SGESC) self-driving testing field. The objective of this testing is to complete a comprehensive functional assessment before the cars are driven on the road.

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Acer Group Founder Stan Shih pointed out that Taiwan has the capability of playing extremely impotant roles in the self-driving car industry such as providing components and subsystems. Furthermore, in open platforms Taiwan can play a leading role, and as the automobile manufacturing industry of today changes to more open platforms, in the future all enterprises will have the capability of assembling self-driving cars independently. In addition, manufacturing components will have better profitibility than manufacturing entire cars.

To date, a testing field for the development of self-driving automobiles and component systems has been developed at SGESC. Covering an area of approximately 1.75 hectares, it simulates low-speed driving conditions of between zero to thirty KMH on domestic roads, and it provides self-driving small-sized cars and medium-sized buses with realistic testing for mixed-traffic in the closed field.

The self-driving car testing field is expected to formally open in the first quarter of 2019, and Acer will become the first industrial partner in the domain of Taiwanese self-driving automobiles.

In September 2018 Acer announced their first independently developed self-driving electric concept car with a driving software system which simultaneously receives data from from image recognition, 3D light obstacle detection, and high-precision maps as well as from real-time positioning technology. Moreover, after processing judgements through the utilization of AI decision-making models, this data will assist in making decisions regarding whether to turn, brake, cruise, avoid obstacles, or stop.

(TR/ Phil Sweeney)

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