Mixed Autonomy

Mixed autonomy research focuses on developing systems where humans and autonomous agents share control, aiming to leverage the strengths of both for improved performance and safety in complex tasks. Current research emphasizes robust methods for dynamically balancing autonomy levels based on context and operator capabilities, often employing reinforcement learning, control theory, and symbolic reasoning within multi-agent frameworks. This field is crucial for advancing robotics, autonomous driving, and human-computer interaction, with applications ranging from assistive technologies to large-scale traffic management and collaborative multi-robot systems.

Papers

November 25, 2022

A Hierarchical Variable Autonomy Mixed-Initiative Framework for Human-Robot Teaming in Mobile Robotics
Dimitris Panagopoulos, Giannis Petousakis, Aniketh Ramesh, Tianshu Ruan, Grigoris Nikolaou, Rustam Stolkin, Manolis Chiou
Mobile Robot AI Agent Remote Human Operator Mixed Autonomy Mixed Initiative Control Paradigm

November 22, 2022

Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
Rodolfo Valiente, Behrad Toghi, Mahdi Razzaghpour, Ramtin Pedarsani, Yaser P. Fallah
Native Robustness Multi Agent Reinforcement Learning Human SAFETY Mixed Traffic Cooperative Behavior Mixed Autonomy Heterogeneous Vehicle

June 28, 2022

The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge
Bo Yan, Leilei Cao, Zhuang Li, Hongbin Wang
Place Solution Segmentation Framework Mixed Autonomy Vision Benchmark

February 2, 2022

Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic
Rodolfo Valiente, Behrad Toghi, Ramtin Pedarsani, Yaser P. Fallah
Reinforcement Learning Native Robustness Autonomous Vehicle Multi Agent Reinforcement Learning Adaptive Importance Human Driven Vehicle Mixed Autonomy Traffic Mixed Autonomy Cooperative Autonomous Driving

December 9, 2021

Learning Generalizable Multi-Lane Mixed-Autonomy Behaviors in Single Lane Representations of Traffic
Abdul Rahman Kreidieh, Yibo Zhao, Samyak Parajuli, Alexandre Bayen
Traffic Efficiency Mixed Autonomy Lane Representation Driving Dynamic Future Autonomous

November 8, 2021

Reinforcement Learning for Mixed Autonomy Intersections
Zhongxia Yan, Cathy Wu
Reinforcement Learning Model Free Reinforcement Learning Traffic Network Mixed Autonomy Mixed Autonomy Traffic

Mixed Autonomy

Papers

A Hierarchical Variable Autonomy Mixed-Initiative Framework for Human-Robot Teaming in Mobile Robotics

Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic

The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge

Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic

Learning Generalizable Multi-Lane Mixed-Autonomy Behaviors in Single Lane Representations of Traffic

Reinforcement Learning for Mixed Autonomy Intersections