Driver models are invaluable for planning in autonomous vehicles as well as validating their safety in simulation. Highly parameterized black-box driver models are very expressive, and can capture nuanced behavior. However, they usually lack interpretability and sometimes exhibit unrealistic-even dangerous-behavior. Rule-based models are interpretable, and can be designed to guarantee "safe" behavior, but are less expressive due to their low number of parameters. In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories. We solve the online parameter estimation problem using particle filtering, and benchmark performance against rule-based and black-box driver models on two real world driving data sets. We evaluate the closeness of our driver model to ground truth data demonstration and also assess the safety of the resulting emergent driving behavior.
Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction
Electrical Engineering department, University of South Florida（佛罗里达州立大学电气工程系）；Department of Biostatistics, Epidemiology and Bioinformatics, University of Pennsylvania（宾夕法尼亚大学生物统计学、流行病学与生物信息学系）；Computer Science and Engineering department, University of South Florida（佛罗里达州立大学计算机科学与工程系）；Mathematics & Statistics, University of South Florida（佛罗里达州立大学数学与统计学系）；Department of Computer Science and Engineering, University of Missouri Science and Technology（密苏里科技大学计算机科学与工程系）；School of Medicine, Stanford University（斯坦福大学医学院）；Department of Computer Science, Rutgers University（罗格斯大学计算机科学系）；Department of Biostatistics, Vanderbilt University（范德比尔特大学生物统计学系）
A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
The University of Sydney（悉尼大学）；SenseTime Computer Vision Group（商汤科技计算机视觉组）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；CUHK MMLab（香港中文大学多媒体实验室）；Institute for AI, Peking University（北京大学人工智能研究院）
