13 citations · 19 across the 6 of their papers we have counts for
5 papers · 1 filter
Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach
Weichao Xu, Huaxin Pei, Jingxuan Yang +3
Recent advances in decision-making policies have led to significant progress in fields such as autonomous driving and robotics. However, testing these policies remains crucial with…
Few-Shot Testing of Autonomous Vehicles with Scenario Similarity Learning
Shu Li, Honglin He, Jingxuan Yang +3
Testing and evaluation are critical to the development and deployment of autonomous vehicles (AVs). Given the rarity of safety-critical events such as crashes, millions of tests ar…
Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems
Ruoxuan Bai, Jingxuan Yang, Weiduo Gong +3
Intelligent systems are increasingly integral to our daily lives, yet rare safety-critical events present significant latent threats to their practical deployment. Addressing this…
Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning
Jingxuan Yang, Ruoxuan Bai, Haoyuan Ji +3
The assessment of safety performance plays a pivotal role in the development and deployment of connected and automated vehicles (CAVs). A common approach involves designing testing…
Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity
Shu Li, Jingxuan Yang, Honglin He +3
Testing and evaluating the safety performance of autonomous vehicles (AVs) is essential before the large-scale deployment. Practically, the number of testing scenarios permissible…