Chinese search engine Baidu will invest $1.5 billion in autonomous driving projects over the next three years.
The Apollo Fund will be used to advance the self-driving industry, an area Baidu has invested heavily in over recent years.
This week Baidu also announced the release of Apollo 1.5, the latest iteration of the company’s Apollo open-source autonomous driving software.
Apollo is described by the company as the “Android of the auto industry.” First announced in April, Apollo is an open platform that acts as a reliable all-in-one solution that supports all major features and functions of an autonomous vehicle.
Its first iteration, Apollo 1.0, was announced in July at Baidu’s inaugurate AI Developers Conference in Beijing (pictured above).
Apollo 1.5 has added five additional core capabilities which include obstacle perception, planning, cloud simulation, high-definition (HD) maps and end-to-end deep learning, providing more comprehensive solutions to developers and ecosystem partners to accelerate the deployment of autonomous driving.
Over the past two months, more than 65,000 lines of new code have been added to Apollo, Baidu said, adding that the response from global developers had been positive. To date, more than 1,300 companies have downloaded Apollo source code and nearly 100 companies have applied for open data via the Apollo website.
Apollo now has 70 global and Chinese partners, including OEMs, Tier 1 suppliers, developer platforms and technology start-ups. Partners include the likes of Nvidia Corp, Microsoft, Bosch, Ford, Intel and TomTom.
Baidu said it has signed over 50 cooperation agreements with Apollo partners on mass production or joint product development plans.
King Long, a Xiamen-based commercial vehicle manufacturer, performed autonomous waypoint driving in enclosed venues using buses deployed with Apollo’s 1.0 capabilities.
Momenta, a Beijing-based autonomous driving start-up, successfully conducted testing on designated lanes using Apollo 1.5 enabled cars, which were able to accurately recognise obstacles, passengers, and make optimal decisions even at night when visibility is very low.