30 citations
- University of MichiganUS10 papers
- Chalmers University of TechnologySE3 papers
- University of Michigan–DearbornUS2 papers
- Virginia TechUS2 papers
- Beijing National Research Center for Information Science and TechnologyCN1 paper
- California University of PennsylvaniaUS1 paper
- DEVCOM Army Research LaboratoryUS1 paper
- Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUS1 paper
- Insurance Institute for Highway SafetyUS1 paper
- Massachusetts Institute of TechnologyUS1 paper
- Ministerium für Verkehr des Landes Nordrhein-WestfalenDE1 paper
- Nomor Research (Germany)DE1 paper
12 papers
Advanced Assistance for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction
Gerui Xu, Boyou Chen, Huizhong Guo +6
Traffic collision reconstruction traditionally relies on human expertise and can be accurate, but pre-crash reconstruction is more challenging. This study develops a multi-agent AI…
Assessing the Effectiveness of Driver Training Interventions in Improving Safe Engagement with Vehicle Automation Systems
Chengxin Zhang, Huizhong Guo, Zifei Wang +3
This study investigates how targeted training interventions can improve safe driver interaction with vehicle automation (VA) systems, focusing on Adaptive Cruise Control (ACC) and…
Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection
Daniel Bogdoll, Rajanikant Patnaik Ananta, Abeyankar Giridharan +3
With an ever-increasing availability of data, it has become more and more challenging to select and label appropriate samples for the training of machine learning models. It is esp…
RAVE Checklist: Recommendations for Overcoming Challenges in Retrospective Safety Studies of Automated Driving Systems
John M. Scanlon, Eric R. Teoh, David G. Kidd +12
The public, regulators, and domain experts alike seek to understand the effect of deployed SAE level 4 automated driving system (ADS) technologies on safety. The recent expansion o…
Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
Henrik Imberg, Xiaomi Yang, Carol Flannagan +1
Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adapti…
Adaptive Safety Evaluation for Connected and Automated Vehicles with Sparse Control Variates
Jingxuan Yang, Haowei Sun, Honglin He +3
Safety performance evaluation is critical for developing and deploying connected and automated vehicles (CAVs). One prevailing way is to design testing scenarios using prior knowle…