354 citations · 843 across the 69 of their papers we have counts for
6 papers · 1 filter
Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data
Ran Tian, Masayoshi Tomizuka, Liting Sun
Reward function, as an incentive representation that recognizes humans' agency and rationalizes humans' actions, is particularly appealing for modeling human behavior in human-robo…
IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data
Xiaosong Jia, Liting Sun, Masayoshi Tomizuka +1
Autonomous vehicles (AVs) need to share the road with multiple, heterogeneous road users in a variety of driving scenarios. It is overwhelming and unnecessary to carefully interact…
Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory
Liting Sun, Wei Zhan, Yeping Hu +1
Understanding human driving behavior is important for autonomous vehicles. In this paper, we propose an interpretable human behavior model in interactive driving scenarios based on…
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real
Jiachen Li, Hengbo Ma, Wei Zhan +1
Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decisio…
A Learning Framework for High Precision Industrial Assembly
Yongxiang Fan, Jieliang Luo, Masayoshi Tomizuka
Automatic assembly has broad applications in industries. Traditional assembly tasks utilize predefined trajectories or tuned force control parameters, which make the automatic asse…
Cascade Attribute Learning Network
Zhuo Xu, Haonan Chang, Masayoshi Tomizuka
We propose the cascade attribute learning network (CALNet), which can learn attributes in a control task separately and assemble them together. Our contribution is twofold: first w…