现在不做VLA和世界模型的公司，还有哪些？
Ranking
No observed public metrics; popularity remains neutral/archived.
Merged summary
TL;DR - A WeChat industry roundup (from 自动驾驶之心) arguing that VLA (vision-language-action) plus world models has become a mandatory technical route for essentially every major autonomous-driving player, followed by a promotion for the account's 14-week paid research mentorship program. Useful mainly as a market-landscape signal on where AD stacks are converging.
- Claimed VLA adopters: Li Auto (Mind-VLA), XPeng (VLA 2.0), DeepRoute (元戎), Xiaomi, Tesla (world model + FSD fusion), Waymo, NVIDIA; BYD and Changan are described as recent entrants.
- Claimed world-model-leaning players: NIO, Huawei, Momenta, Pony.ai, WeRide — with few public papers; Geely, GAC, SAIC, Chery are said to have no public progress, which the author attributes to production-timeline KPIs rather than absence of R&D.
- The author's framing: direction is settled, differentiation lies in the entry point — how VLA reasoning couples with world-model future prediction, how action generation and physics modeling share representations, and on-vehicle stability.
- The bulk of the post is course marketing: 14 weeks (12 research + 2 writing), baselines VAD/UniAD/DiffusionDrive/OpenDriveVLA/Senna, datasets nuScenes/Waymo/Argoverse, 6 seats, 4090-class inference and 4–8 GPU training required. Note: promotional content, no original results or benchmarks.
Sources (1)
现在不做VLA和世界模型的公司，还有哪些？
TL;DR - A WeChat industry roundup (from 自动驾驶之心) arguing that VLA (vision-language-action) plus world models has become a mandatory technical route for essentially every major autonomous-driving player, followed by a promotion for the account's 14-week paid research mentorship program. Useful mainly as a market-landscape signal on where AD stacks are converging.
- Claimed VLA adopters: Li Auto (Mind-VLA), XPeng (VLA 2.0), DeepRoute (元戎), Xiaomi, Tesla (world model + FSD fusion), Waymo, NVIDIA; BYD and Changan are described as recent entrants.
- Claimed world-model-leaning players: NIO, Huawei, Momenta, Pony.ai, WeRide — with few public papers; Geely, GAC, SAIC, Chery are said to have no public progress, which the author attributes to production-timeline KPIs rather than absence of R&D.
- The author's framing: direction is settled, differentiation lies in the entry point — how VLA reasoning couples with world-model future prediction, how action generation and physics modeling share representations, and on-vehicle stability.
- The bulk of the post is course marketing: 14 weeks (12 research + 2 writing), baselines VAD/UniAD/DiffusionDrive/OpenDriveVLA/Senna, datasets nuScenes/Waymo/Argoverse, 6 seats, 4090-class inference and 4–8 GPU training required. Note: promotional content, no original results or benchmarks.