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the end of the "echelon": who will be the leader in the era of intelligence?

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in his speech at the teda forum, gu huinan clearly pointed out that lidar will be standard equipment for future vehicles, and gac aion plans to launch l4 robotaxi next year. great wall motors also did not hesitate to declare itself the "first" in the leading position and demonstrated the size of its intelligent driving team.

but the reality is not that simple.

faced with the rapid development of technology, self-developed researchers have to bear tremendous pressure.

first, there is a huge difference in capital investment. baidu invested more than 150 billion yuan, tesla spent 28 billion yuan to build a computing center, and byd has achieved a certain breakthrough in the scale of its intelligent driving team. behind these figures is the cruel reality of industry competition.

the second is the sense of urgency of the time node. industry insiders generally believe that 2024 will be the first year of intelligent driving, and 2025 will usher in more intense competition. yu kai, founder of horizon robotics, predicted that in 2025, urban noa will open a new window of competition, and the dream of "driverless driving" is getting closer step by step.

however, even at such a tight pace, the efforts of independent researchers still fail to escape the predicament.

they need to overcome cost and time pressures and continuously improve their technical levels in order to compete fairly with leading intelligent driving companies.

this is like a silent war. both sides are actively preparing, but in the end, who will win?

the word "echelon" seems to have been subverted in the evolution of the field of autonomous driving. from the huge scale of investment by technology giants to the sense of urgency at time nodes, it reflects that a new competitive landscape is taking shape.

perhaps, the real "winner" is not the company that relies solely on technology, but the company that can continuously innovate, constantly adjust its strategy, and ultimately find the best path.