Automotive Simulation Meets AI
Welcome to the first edition of our latest newsletter: Automotive Simulation Meets AI. Read Tamás Matuszka’s blog about neural reconstruction in AD simulation. If you like this blog and enjoy reading technical texts on AI, neural rendering, and simulation, consider subscribing to this newsletter!
Applying neural reconstruction in simulation for automated driving
Validating automated driving software requires millions of test kilometers. This not only implies long system development cycles with continuously increasing complexity, but it also brings with it the problem that real-world testing is resource intensive, and safety issues might arise as well. A virtual validation suite like aiSim can alleviate these burdens of real-world testing.
Automated driving (AD) and Advanced Driver Assistance Systems (ADAS) rely on closed-loop validation to ensure safety and performance. However, achieving closed-loop evaluation requires a 3D environment that accurately represents real-world scenarios. While these 3D environments can be built manually by 3D artists, these solutions have limitations in scalability and addressing the Sim2Real domain gap.
Neural Rendering — Bridging the Gap
Neural rendering can mitigate this issue by leveraging deep learning techniques, it can realistically render the static (and dynamic) environments from novel viewpoints. Let’s explore the pros and cons of this approach:
Pros:
- Photorealistic Quality: Neural rendering produces almost photorealistic scenes, enhancing realism.
- Data-Driven and Scalable: This approach is scalable, making it suitable for real-time applications (such as 3D Gaussian Splatting).
Cons:
- Out-of-Distribution Objects: Neural rendering struggles with inserting out-of-distribution (i.e., previously unseen) objects into the 3D environment.
- Artifact Impact on Dynamic Objects: Artifacts may affect the appearance of dynamic objects.
- Geometric Inconsistencies: Geometric inconsistencies might arise — mostly in depth-prediction.
