Test Scenario

Test scenario generation and evaluation are crucial for robustly verifying the performance and safety of complex systems, particularly in autonomous driving, AI models, and software applications. Current research focuses on developing automated methods for generating diverse and challenging scenarios, often employing techniques like Bayesian optimization, genetic algorithms, and generative models to optimize scenario selection based on coverage, similarity, and challenge metrics. This work is significant because it enables more efficient and comprehensive testing, leading to improved system reliability and safety across various domains, from autonomous vehicles to large language models.