51 citations · 96 across the 20 of their papers we have counts for
7 papers · 1 filter
Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios
Kaiwen Liu, Nan Li, H. Eric Tseng +2
Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with…
Stochastic MPC with Multi-modal Predictions for Traffic Intersections
Siddharth H. Nair, Vijay Govindarajan, Theresa Lin +3
We propose a Stochastic MPC (SMPC) formulation for autonomous driving at traffic intersections which incorporates multi-modal predictions of surrounding vehicles for collision avoi…
Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization
Lu Wen, Songan Zhang, H. Eric Tseng +3
Meta Reinforcement Learning (Meta-RL) has seen substantial advancements recently. In particular, off-policy methods were developed to improve the data efficiency of Meta-RL techniq…
Sensitivity Analysis of Passenger Behavioral Model for Dynamic Pricing of Shared Mobility on Demand
Vineet Jagadeesan Nair, Yue Guan, Anuradha M. Annaswamy +2
This paper provides a framework to quantify the sensitivity associated with behavioral models based on Cumulative Prospect Theory (CPT). These are used to design dynamic pricing st…
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
Songan Zhang, Lu Wen, Huei Peng +1
It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endang…
Models and Predictive Control for Nonplanar Vehicle Navigation
Thomas Fork, H. Eric Tseng, Francesco Borrelli
We present a simplified model of a vehicle driving on a nonplanar road. A parametric surface is used to describe the nonplanar road which can describe any combination of curvature,…
Safe Reinforcement Learning Using Robust Action Governor
Yutong Li, Nan Li, H. Eric Tseng +3
Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. This hinders the ap…