7 citations · 7 across the 6 of their papers we have counts for
7 papers
Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles, Part I: Methodology
Henry X. Liu, Tinghan Wang, Xintao Yan +6
Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional fu…
Efficient Safety Verification of Autonomous Vehicles with Neural Network Operator
Lingxiang Fan, Linxuan He, Haoyuan Ji +1
When autonomous vehicles encounter untrained scenarios, ensuring safety hinges on effective safety verification to prevent accidents stemming from unexpected model decisions. Reach…
TeraSim-World: Worldwide Safety-Critical Data Synthesis for End-to-End Autonomous Driving
Jiawei Wang, Haowei Sun, Xintao Yan +3
Safe and scalable deployment of end-to-end (E2E) autonomous driving requires extensive and diverse data, particularly safety-critical events. Existing data are mostly generated fro…
Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles
Henry X. Liu, Xintao Yan, Haowei Sun +7
Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional fu…
Challenger: Affordable Adversarial Driving Video Generation
Zhiyuan Xu, Bohan Li, Huan-ang Gao +7
Generating photorealistic driving videos has seen significant progress recently, but current methods largely focus on ordinary, non-adversarial scenarios. Meanwhile, efforts to gen…
Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach
Hao Lyu, Yanyong Guo, Pan Liu +3
Recently, artificial intelligence (AI)-enabled nonlinear vehicle platoon dynamics modeling plays a crucial role in predicting and optimizing the interactions between vehicles. Exis…
Evaluation of automated driving system safety metrics with logged vehicle trajectory data
Xintao Yan, Shuo Feng, David J. LeBlanc +2
Real-time safety metrics are important for the automated driving system (ADS) to assess the risk of driving situations and to assist the decision-making. Although a number of real-…