Driverless cars are transporting pupils are a school in China along a 3km mixed traffic route.
Autonomous vehicle specialist, Venti Technologies, has successfully deployed two autonomous SUVs built by China manufacturer, SAIC-GM-Wuling Automobile (SGMW) and General Motors (GM).
Two of the autonomous vehicles were deployed at a school in Nanning City, the capital of Guangxi Province, China. Operating at a maximum speed of 15 kph, the cars provide shuttle transport services for students and visitors, and are easily booked via a hailing app for destinations along a 9-station loop.
Flexible, algorithmic-based autonomous vehicle technology has been installed in the cars, which run on a 3Km loop in opposite directions. The Company’s sensor configuration eliminates blind spots and is able to operate with mixed traffic and other road users including, for example, cars, scooters and pedestrians. The Venti-enabled SUVs are also able to overtake lower speed vehicles while avoiding vehicles coming in the other direction.
Xinxin Du, Founder and CTO commented, “The successful autonomous SUV deployment is a key milestone for Venti and showcases the breadth of opportunities for our technology. Passenger vehicles require very nimble and precise operations. We are delighted to be moving forward with SGMW, a major mass volume producer of cars, microvans and other vehicles in China.”
Venti’s autonomous mobility platform surpasses slow, inflexible situation-based-coded systems through Deep Learning and Model Predictive Control algorithms. Venti-enabled vehicles map as they go, adapting to complex environments and challenging situations through algorithms that use abstract information constructs. Venti autonomous vehicles see small obstacles such as a drink container on the road, and around corners. The AVs do not need lane markers or beacons, can drive forwards and backwards, can do three- and five-point-turns and have state-of-the-art rain operation. Venti’s system is the first in the world to improve both obstacle detection and vector velocity estimation accuracy by merging the analyses to enable AVs to drive safely and reliably in complex environments.