Li Auto recently set up an end-to-end large model autonomous driving team of slightly over 200 people, according to local media.
Li Auto (NASDAQ: LI) has reportedly set up a dedicated team to bet on end-to-end AI (artificial intelligence) technology, becoming the latest to do so after Nio (NYSE: NIO).
Li Auto recently set up an end-to-end large-model autonomous driving team of just over 200 people, with some members of other teams providing flexible support, local media outlet 36kr reported today.
The car company isn't alone in doing so, as Nio has previously led the way in making organizational changes by setting up a department dedicated to end-to-end large models, the report noted.
Li Auto's smart driving team is divided into two main groups: algorithm development and mass production development, with a team size of about 800 people, the report said.
Li Xiang, the company's founder, chairman, and CEO, has said that by the end of this year or early next year, Li Auto will launch an end-to-end +VLM (Vision-Language Model) autonomous driving solution that will be trained by over 10 million clips.
Tesla (NASDAQ: TSLA) released FSD V12 earlier this year, which brought good results. This has led to an industry consensus on the end-to-end large AI model used by Tesla, and more car companies in China are starting to experiment with this route.
Nio's smart driving R&D department completed a team restructuring to focus more on end-to-end technology, according to a June 19 report in local media outlet LatePost.
Under the original commonly adopted technology architecture, for smart driving systems, modules such as perception, prediction, decision-making, and control all require engineers in different fields.
The end-to-end smart driving system uses sensor data as input and is used directly in the vehicle's control commands, with all intermediate processes relying on neural network models.
On July 11, Nio started rolling out the Banyan 2.6.5 CN system for vehicles based on the NT 2.0 technology platform, bringing AEB (automatic emergency braking) functionality optimized using end-to-end technology.
It is worth noting that end-to-end technology is demanding in terms of computing power resources.
Li Auto is scrambling to get more training computing power after choosing the end-to-end solution, 36kr's report today said, adding that an insider said Li Auto believes the next point of competition for smart driving is supercomputing centers.
Li Auto purchased more than 300 Nvidia servers last year from Volcano Engine, a cloud service platform owned by ByteDance, and is also currently working with cloud service vendors including Alibaba Cloud and Baidu Cloud, according to the report.
Huawei currently has the largest smart driving training computing power in China, mentioning in June that its computing power would reach 3.5 EFLOPS, the report noted.
Xpeng's (NYSE: XPEV), Nio's, and Li Auto's smart driving centers have computing powers of 0.6 EFLOPS, 1.4 EFLOPS, and 1.4 EFLOPS, respectively, the report said.
Nio reportedly reshuffles smart driving team to focus more on end-to-end tech
据当地媒体报道，理想汽车最近成立了一个由200多人组成的端到端大型自动驾驶团队。
Li Auto(NASDAQ: LI)成为继Nio(NYSE: NIO)之后，最新成立一支专门押注于端到端的人工智能技术的团队，你也应该遵循所有规则来产生压力位的支持。
据36氪今日报道，理想汽车最近成立了一个由约200人组成的端到端大模型自动驾驶团队，并且一些来自其他团队的成员提供灵活的支持。
据该报告指出，这家汽车公司并不是唯一这样做的公司，此前，Nio已经领先进行组织架构变革，成立了一个专门的部门来致力于端到端大模型。
报告指出，理想汽车的智能驾驶团队分为两个主要组：算法开发和量产开发，由约800人组成。
该公司的创始人、董事长和CEO李想表示，到今年年底或明年初，理想汽车将推出一款经过超过1000万个剪辑训练的端到端+ VLm（视觉语言模型）自主驾驶解决方案。
特斯拉(NASDAQ:TSLA)今年早些时候推出了FSD V12，并取得了不错的成绩，这导致了关于特斯拉使用的端到端大型人工智能模型的行业共识，中国的更多汽车公司开始尝试这条路线。
据6月19日当地媒体LatePost报告，Nio的智能驾驶研发部门完成了一个团队重组，以更加专注于端到端技术。
按照最初通行的技术架构，对于智能驾驶系统来说，诸如感知、预测、决策和控制等模块都需要来自不同领域的工程师。
端到端智能驾驶系统使用传感器数据作为输入，并直接用于车辆的控制命令，所有中间过程都依赖于神经网络模型。
7月11日，Nio开始在基于Nt2.0技术平台的车辆上推出Banyan 2.6.5 CN系统，利用端到端技术优化了AEb（自动紧急制动）功能。
值得注意的是，端到端技术在计算能力资源方面要求很高。
36kr今日的报道称，理想汽车正在争分夺秒地获取更多的培训计算能力，而他们选择了端到端解决方案，并且内部人士称，理想汽车认为智能驾驶的下一个竞争环节是超级计算中心。
据该报告称，理想汽车去年从字节跳动所拥有的云服务平台“Volcano Engine”购买了超过300台英伟达服务器，并且目前也在与包括阿里巴巴云和百度云在内的云服务提供商合作。
该报告提到，华为目前在中国拥有最大的智能驾驶训练计算能力，该公司在6月份提到它的计算能力将达到3.5EFLOPS。
据该报告称，小鹏汽车（NYSE:XPEV）、Nio以及理想汽车的智能驾驶中心拥有0.6 EFLOPS、1.4 EFLOPS以及1.4 EFLOPS的计算能力。
据报道，蔚来汽车正在重新调整智能驾驶团队，更加注重端到端技术的发展。
