StartupDrivingLondon, United KingdomFounded 2017
Develops end-to-end autonomous driving models (Wayve AI Driver, LINGO vision-language-action models) and the GAIA generative world models for driving simulation and evaluation.
Wayve is a London-based company founded in 2017 that develops end-to-end AI driving software (embodied AI) for automated and autonomous vehicles, licensed to carmakers and fleet operators. Its investors include SoftBank, Microsoft, NVIDIA, Uber and several automakers. In February 2026 it raised a $1.2B Series D at an $8.6B post-money valuation.
Private company · $2.51B disclosed in USD across 4 of 4 listed rounds
| Date | Round | Amount | Lead | Source |
|---|---|---|---|---|
| 18 Jan 2022 | Series B | $200M | Eclipse Ventures | techcrunch.com, 18 Jan 2022 |
| 7 May 2024 | Series C | $1.05B | SoftBank Group | wayve.ai, 7 May 2024 |
| 25 Feb 2026 | Series DValuation: $8.6B post-money | $1.2B | Eclipse, Balderton, SoftBank Vision Fund 2 | wayve.ai, 25 Feb 2026 |
| 15 Apr 2026 | Series D extension | $60M | n/a | wayve.ai, 15 Apr 2026 |
#60 of 104 model companies · score 25.0 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 0 of 5 listed models have open weights.
The score (0–100) measures public, checkable evidence about a company's robot models. It does not measure private capability. A company that publishes no benchmark results and no weights can still have strong models. Every input is computed from the data on each company page, and every number there links to its source.
| Input | Weight | How it is measured |
|---|---|---|
| Published benchmarks | 35% | For each scored benchmark, the company's best result divided by the best result any company has published, averaged over the benchmarks it reports. No published results scores 0. Results come only from the model's own paper, model card or the official benchmark leaderboard. Scored: LIBERO (4-suite average success, %); SimplerEnv Google Robot, visual matching (%); SimplerEnv Google Robot, variant aggregation (%); SimplerEnv WidowX / Bridge (%); CALVIN ABC→D (average length, 0–5); RoboArena (pairwise rating). |
| Open weights | 20% | Share of the company's listed models whose weights the public can download. |
| Adoption | 20% | GitHub stars on the company's most-starred official model repository, on a log scale relative to the most-starred repository in the list. Hugging Face downloads are shown on company pages but not scored, because gated repositories report zero. |
| Released models | 15% | Number of publicly announced, named models, capped at 5. |
| Disclosed funding | 10% | The sum of the funding rounds listed on the company's profile that were disclosed in USD, on a log scale from $10M to the largest sum in the list. Rounds reported only in other currencies are listed but not converted or counted. This input applies only to private startups and robot makers. For big-tech divisions, research labs and listed companies it is n/a, and its weight is shared across the other inputs in proportion. |
Sources. arXiv papers and official model cards for benchmarks and parameters; official leaderboards (RoboArena); the GitHub and Hugging Face APIs for stars and downloads; company announcements and established press for funding. Missing values are shown as n/a, never estimated.
Updated 19 Oct 2026. Benchmark protocols differ between papers, for example in fine-tuning and number of trials, so small gaps between close scores are not meaningful.
Closed weightsWorld model
Multimodal (camera and radar) world model for closed-loop simulation with the Wayve AI Driver in the loop, built from recorded sensor data without HD maps.
No benchmark results on a shared public benchmark have been published for this model.
Closed weightsWorld model
Latent-diffusion world model aimed at offline evaluation of driving policies, including counterfactual ego trajectories and cross-rig scene transfer.
No benchmark results on a shared public benchmark have been published for this model.
Closed weightsWorld model
Controllable multi-camera latent-diffusion world model for generating driving scenarios for training and evaluation.
No benchmark results on a shared public benchmark have been published for this model.
Closed weightsVLA
Closed-loop vision-language-action driving model that outputs a trajectory and language commentary; tested on public roads.
No benchmark results on a shared public benchmark have been published for this model.
Closed weightsWorld model
Generative world model that produces driving video conditioned on video, text and action inputs.
No benchmark results on a shared public benchmark have been published for this model.
