Chinese Internet company Baidu.com Inc. said it plans to build the world’s largest autonomous ride-hailing service area in 2023, maintaining its growth momentum.
The company, which offers autonomous ride-hailing platform Apollo Go, outlined its plans during its autonomous driving tech event, Apollo Day.
Further, a series of new autonomous driving technology breakthroughs were revealed at the event. These include an AI big model built for autonomous driving perception, high-definition autonomous driving maps, a closed-loop autonomous driving data system, and end-to-end adaptation of AI chips for autonomous vehicles.
In August, Baidu said it secured China’s first-ever permits for commercial fully driverless ride-hailing services. China was the first country in the world that allowed fared fully driverless robotaxi operation.
Since then, the company has already rolled out fully driverless ride-hailing services, with no human drivers in the car, in the cities of Chongqing and Wuhan. They have access to hundreds of square kilometers of operation area.
The company, which considers Apollo Go to be the world’s biggest robotaxi service provider, now plans to continue to expand its operation area next year to build the world’s largest service area for fully driverless robotaxi service.
Apollo Go currently covers more than 10 cities in China including all first-tier cities. In the third quarter alone, Apollo Go has completed more than 474,000 rides, up 311 percent year over year, and a 65 percent increase compared to last quarter. By the end of third quarter, the accumulated rides provided to the public by Apollo Go have reached 1.4 million.
Jingkai Chen, Baidu’s autonomous driving technology expert, said at the event, “Backed by its solid AI technology, Baidu Apollo has created a safe, intelligent and efficient autonomous driving technology system, bringing robotaxi services from designated zones to open roads at scale.”
Jian Ouyang, CEO of Kunlun Chip, also revealed that Baidu’s 2nd-gen Kunlun AI chip has completed an end-to-end performance adaptation for autonomous driving.
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