Jiangfan Liu

Real-time risk assessment in autonomous driving just got a major upgrade, achieving state-of-the-art performance with a novel framework that combines perception and structured reasoning.

SafeGen boosts VLMAD performance by over 24% in safety-critical scenario generation, bridging the sim-to-real gap that has long plagued autonomous driving systems.

Adversarial attacks on vision-language agents reveal critical vulnerabilities, with multi-view optimization strategies proving significantly more effective than isolated approaches.