This type of PEV is shaped like a three-wheeled automatic car, and it has radar detection and camera and GPS orientation. It is capable of carrying passengers or cargo, and by just using the corresponding App, “Spike,” the car can be summoned to designated locations.

The car is equipped with an electronic power assistance device and pedal, which enables users to choose to drive autonomously or to enjoy being taken for a car ride. At the same time, the internal GPS position information and detection devices can calculate the shortest routes and slow down the car or change routes in response to road conditions.

MIT has emphasized that PEV features lightweight bicycle concepts, and other than the care frame, which is from the United States, ninety percent of the vehicle’s body was made in collaboration with local Taiwanese groups. Each vehicle will cost approximately $200,000.

III stated that PEV is a big step forward for Taiwan in Internet of Vehicles development, and through the use of sensor technology and cloud data processing, when the car is in the process of moving it will be able to have precise control over nearby cars and information about road conditions while also providing customized precision services.

MIT Media Lab research assistant Lin Jia-liang stated that corresponding Apps will be able to be used to calculate the shortest routes and to summon the vehicles to set locations while also reducing the number of detours during driving. In addition, the cars will be able to collect environment data and user information and upload it to the cloud.

PEVs can replace fossil fuel power with electrical power. Lin Jia-liang said that compared to other driverless cars, the PEV simulations in New York for two hours of delivering cargo reduced driving distance by forty percent and carbon emissions by sixty percent.

Lin Jia-liang stated that at the end of this year MIT Media Lab will manufacture fifty vehicles and conduct field testing in the following five cities: Taipei, Singapore, Hamburg, Germany, and the United States in order to assist big data cloud collection about conditions in each city.

(TR/ Phil Sweeney)