5 citations · 6 across the 4 of their papers we have counts for
5 papers
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…
Dynamically Expanding Capacity of Autonomous Driving with Near-Miss Focused Training Framework
Ziyuan Yang, Zhaoyang Li, Jianming Hu +1
The long-tail distribution of real driving data poses challenges for training and testing autonomous vehicles (AV), where rare yet crucial safety-critical scenarios are infrequent.…
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…