SimScale synthesizes unseen driving states from real logs via neural rendering and reactive environments, generates pseudo-expert trajectories, and shows that co-training on real plus simulated data improves planning robustness and generalization on real benchmarks, with gains scaling by simulation
Safemvdrive: Multi-view safety-critical driving video synthesis in the real world domain.arXiv preprint arXiv:2505.17727, 2025
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Industry practitioners identified 12 ADS testing challenges, prioritized two for end-to-end systems, and found that most of the 17 examined research studies lack direct applicability to real industrial contexts.
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SimScale: Learning to Drive via Real-World Simulation at Scale
SimScale synthesizes unseen driving states from real logs via neural rendering and reactive environments, generates pseudo-expert trajectories, and shows that co-training on real plus simulated data improves planning robustness and generalization on real benchmarks, with gains scaling by simulation
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From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry
Industry practitioners identified 12 ADS testing challenges, prioritized two for end-to-end systems, and found that most of the 17 examined research studies lack direct applicability to real industrial contexts.