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Improved risk-aware motion planning reduces vehicle-limit violations in urban driving scenarios while maintaining safety and efficiency.
A 4B-parameter model outperforms Gemini-3-Pro in autonomous driving by incorporating physics-informed constraints and style-aware training, suggesting specialized models can surpass larger, general-purpose models in domain-specific tasks.
By learning where to sample, autonomous vehicles can achieve up to 84% faster motion planning in complex urban environments without sacrificing safety or success rates.
