Autonomous vehicles must operate safely in an unpredictable world, yet hazardous driving scenarios – often rare and complex – pose significant challenges for AI-driven perception and decision-making systems. This presentation explores advanced algorithmic approaches to identify, categorize, and test these critical edge cases in large-scale driving datasets. We will discuss techniques such as reinforcement learning, scenario mining, and adversarial testing to systematically uncover and validate hazardous scenarios. By enhancing our ability to detect and address these challenges, we can improve the robustness and safety of autonomous driving systems.
In this presentation, you will learn more about:
- How AI-driven methods can efficiently search vast datasets to identify rare but critical hazardous scenarios
- Techniques for synthesizing and validating realistic high-risk situations to improve AV robustness
- Strategies to align data-driven scenario discovery with regulatory and industry safety standards
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