Search Results for author:
Found 13 papers, 7 papers with code
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
This paper presents an online smooth-path lane-change control framework.
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
This approach exposes the agent to a broad variety of behaviors during training, which promotes learning policies that are robust to model discrepancies.
Safe Reinforcement Learning on Autonomous Vehicles
There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safety critical applications.
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers.
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels.
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Navigating urban environments represents a complex task for automated vehicles.
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
We propose a generic approach to enforce probabilistic guarantees on an RL agent.
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios
In this paper, we proposed an interaction-aware decision making with adaptive strategies (IDAS) approach that can let the autonomous vehicle negotiate the road with other drivers by leveraging their cooperativeness under merging scenarios.
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment.
Collaborative Planning for Mixed-Autonomy Lane Merging
Driving is a social activity: drivers often indicate their intent to change lanes via motion cues.
Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations
This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios.
Decomposition Methods with Deep Corrections for Reinforcement Learning
In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently.
