313 citations · 498 across the 41 of their papers we have counts for
8 papers · 1 filter
Monte-Carlo Tree Search for Policy Optimization
Xiaobai Ma, Katherine Driggs-Campbell, Zongzhang Zhang +1
Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free met…
Online monitoring for safe pedestrian-vehicle interactions
Peter Du, Zhe Huang, Tianqi Liu +5
As autonomous systems begin to operate amongst humans, methods for safe interaction must be investigated. We consider an example of a small autonomous vehicle in a pedestrian zone…
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control
Peixin Chang, Shuijing Liu, Haonan Chen +1
We explore the interpretation of sound for robot decision making, inspired by human speech comprehension. While previous methods separate sound processing unit and robot controller…
Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation
Anthony Corso, Peter Du, Katherine Driggs-Campbell +1
Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle va…
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
Carl-Johan Hoel, Katherine Driggs-Campbell, Krister Wolff +2
Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with oth…
Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning
Masha Itkina, Katherine Driggs-Campbell, Mykel J. Kochenderfer
A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles. A r…
Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
Raunak P. Bhattacharyya, Derek J. Phillips, Changliu Liu +3
Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers. However, it is challenging to capture emergent traffi…
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning
Xiaobai Ma, Katherine Driggs-Campbell, Mykel J. Kochenderfer
To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the env…