Wayve inicia testes rodoviários em Tóquio com Nissan Ariya EVs

Wayve acelera os testes de direção autônoma nas ruas de Tóquio usando veículos elétricos Nissan Ariya.

Tecnologia em ação

A empresa de inteligência artificial britânica demonstra seu sistema de condução autônoma em um dos ambientes urbanos mais complexos do mundo. Os Nissan Ariya equipados com tecnologia Wayve navegam pelo tráfego denso e padrões de pedestres únicos de Tóquio.

Parceria estratégica

A colaboração com a Nissan marca um passo significativo na expansão global da tecnologia de veículos autônomos. Os testes em Tóquio seguem implementações bem-sucedidas em outras cidades principais.

Futuro da mobilidade

Os dados coletados durante esses testes rodoviários urbanos alimentarão o desenvolvimento contínuo dos algoritmos de IA da Wayve. A empresa busca superar os desafios restantes da condução autônoma em ambientes imprevisíveis.

Enquanto os veículos autônomos avançam, os investidores tradicionais ainda tentam entender como blockchain poderia revolucionar os sistemas de pagamento de mobilidade - mas isso é conversa para outro dia.

Nissan is developing its own autonomous driving features

Nissan has been developing its own driver-assist and autonomous features, including trials held in London. Choosing to add Wayve’s system is a notable vote of confidence for the British firm. In Nissan cars, the feature will be an “eyes on, hands off” setup, the industry’s Level 2.

The vehicle can handle speed and steering in some conditions, but the human driver must be alert and ready to take back control at any time.

Kendall said so-called “eyes off” systems, in which the car takes full control, are “very similar from an AI perspective.” Robotaxis run by rival software are already in service in parts of the U.S. and China, though operators have faced issues when vehicles meet unusual obstacles or unexpected events.

Toyota Motor Corp. has a partnership with Waymo, Google’s autonomous driving unit. Waymo has also entered Japan, though operations there remain in the testing phase.

Other major carmakers pushing automated driving include Honda Motor Co., General Motors, and Mercedes-Benz. Companies from outside the traditional auto industry are also in the mix, including Amazon and its subsidiary Zoox.

Nissan’s push comes as the wider Japanese auto market faces pressure tied to tariffs imposed by President Donald Trump. Nissan, in particular, has struggled. It has cut jobs and named a new chief executive, Ivan Espinosa, to lead a turnaround effort. The maker of the March compact, the Leaf electric car, and the Infiniti luxury brand posted losses for the April–June period, after a full fiscal year in the red.

Wayve’s tests in Tokyo are the company’s most visible step so far and a move toward its 2027 consumer goal.

Wayve signed a deal with Nvidia

On Friday, Cryptopolitan reported that Wayve and Nvidia have signed a letter of intent that could lead to a $500 million investment in its next financing round.

The startup has already raised $1.3 billion from investors, including Japan’s SoftBank, money that is being used to expand in the U.S., Germany, and Japan, as well as to grow the team in London.

Nvidia supplies the computing hardware that runs Wayve’s software in vehicles and the much larger systems used for training in data centers. Cars equipped with Wayve’s system carry one or two Nvidia chips.

Many more chips are used off the road to train a foundation model on large volumes of driving data, including video of drivers dealing with real streets and unpredictable situations.

Kendall, 33, is a New Zealander who started Wayve in 2017 after studying deep learning for computer vision and robotics at the University of Cambridge.

“We want to build a trillion-dollar company,” Kendall said. He added that the company had reached “a real inflection point in the capabilities of this technology,” which has helped the system learn quickly how to manage Tokyo’s crowded roads.

Wayve’s method relies on models that learn to drive by finding patterns in huge amounts of video and other driving data, and then applying those patterns to new situations. That stands apart from approaches that sought to code detailed rules and maps into the stack for each scenario.

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