86 citations · 221 across the 21 of their papers we have counts for
22 papers
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
Sangjae Bae, David Isele, Alireza Nakhaei +5
This paper presents an online smooth-path lane-change control framework. We focus on dense traffic where inter-vehicle space gaps are narrow, and cooperation with surrounding drive…
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Jiachen Li, David Isele, Kanghoon Lee +3
Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural netwo…
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Kanghoon Lee, Jiachen Li, David Isele +3
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior p…
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
Xiaobai Ma, David Isele, Jayesh K. Gupta +2
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requ…
Risk-Aware Lane Selection on Highway with Dynamic Obstacles
Sangjae Bae, David Isele, Kikuo Fujimura +1
This paper proposes a discretionary lane selection algorithm. In particular, highway driving is considered as a targeted scenario, where each lane has a different level of traffic…
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
Xiaobai Ma, Jiachen Li, Mykel J. Kochenderfer +2
Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios. However, identifying the subtle cues that can indicate d…