ADD runs in parallel with your existing stack — passive, non-interfering. It has been validated across 2,262 simulation scenarios. The next step is real-world data — and we want to do that with you.
We start with a brief email exchange to understand your current ADS stack, perception output fields, and evaluation goals. Based on your perception schema, we adapt the bridge layer to your data format — you don't rewrite your stack to fit ours.
Async · EmailADD is deployed alongside your existing system. It receives the same perception data and produces its own decision output — no connection to vehicle actuators. Your current system remains in sole control.
1–3 days · On-site or remoteRun ADD Studio against your scenario library — or build new scenarios on the spot using the Scene Editor. The SDS Test Runner outputs side-by-side decision comparison, latency benchmarks, and full traceability logs for each case.
For teams running ADD on real-world perception data for the first time, this evaluation becomes a co-validation — your data strengthens the system, and your team gets first-mover insight into its real-world behaviour.
1–2 weeks · Your dataStructured report covering decision accuracy, latency benchmarks, jurisdiction switching results, and ISO 26262 traceability output. Presented to your technical leadership — no commitment required.
Joint review · No obligationReplacing or augmenting an existing decision layer with a certifiable, white-box alternative ahead of a production programme milestone or 2026 safety regulation deadline.
Adding ADD as the pre-constraint layer to an existing ADAS or AD platform — improving ISO 26262 readiness and reducing type-approval documentation overhead.
Tell us about your project. We will get back to you as soon as possible.
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You have an engineering team and want full control. We provide ADD Studio, the rule authoring toolchain, the scenario editor, and the full acceptance suite. Your engineers write the rules, build the scenarios, and iterate at their own pace.
Best for teams who want to own the driving policy from day one.
If you already have your own rulebook — traffic regulations, defensive driving policies, or proprietary behaviour guidelines — hand it to us directly. We translate your rules into the engine, load them, and hand back a system ready for your road test.
No onboarding required on your side. You bring the rules, we bring the engineering.
ADD has been rigorously validated in simulation — 2,262 test items, 10 core scenarios, all green. What comes next is real-world perception data, and we are actively seeking a small number of OEM and Tier-1 partners to run the first production-environment evaluations together.
Early partners get direct access to the core team, input into the roadmap, and the opportunity to shape how ADD integrates with real vehicle stacks — before it becomes a standard offering.
ADD-AV was founded by Shaobo Qiu, who previously served as Director of Autonomous Driving at FAW (First Automobile Works) Technical Center, and as a Research Fellow at the Robotics Center of the University of Electronic Science and Technology of China (UESTC), Chengdu.
The project is developed in collaboration with the School of Vehicle and Mobility, Tsinghua University, and is under the technical guidance of Academician Li Jun of Tsinghua University, a leading authority in automotive engineering and intelligent vehicle systems in China.