Physics-Informed Particle-Based Reinforcement Learning for Autonomy in Signalized Intersections
M Emamifar, SF Ghoreishi - International Journal of Intelligent …, 2024 - Springer
In this paper, we develop a framework to enhance the control of autonomous vehicles within
signalized intersections by integrating system dynamics with imperfect sensor data …
signalized intersections by integrating system dynamics with imperfect sensor data …
Spatiotemporal learning of multivehicle interaction patterns in lane-change scenarios
Interpretation of common-yet-challenging inter-action scenarios can benefit well-founded
decisions for autonomous vehicles. Previous research achieved this using their prior …
decisions for autonomous vehicles. Previous research achieved this using their prior …
Navigating Autonomous Vehicle on Unmarked Roads with Diffusion-Based Motion Prediction and Active Inference
This paper presents a novel approach to improving autonomous vehicle control in
environments lacking clear road markings by integrating a diffusion-based motion predictor …
environments lacking clear road markings by integrating a diffusion-based motion predictor …
Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach
This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
Neuro-symbolic deep reinforcement learning for safe urban driving using low-cost sensors.
M Albilani - 2024 - theses.hal.science
The research conducted in this thesis is centered on the domain of safe urban driving,
employing sensor fusion and reinforcement learning methodologies for the perception and …
employing sensor fusion and reinforcement learning methodologies for the perception and …
Modeling Interaction-Aware Driving Behavior using Graph-Based Representations and Multi-Agent Reinforcement Learning
F Konstantinidis, M Sackmann… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
Modeling the driving behavior of traffic partici-pants in highly interactive traffic situations,
such as roundabouts, poses a significant challenge due to the complex interactions and the …
such as roundabouts, poses a significant challenge due to the complex interactions and the …
Robust ai driving strategy for autonomous vehicles
There has been significant progress in sensing, perception, and localization for automated
driving, However, due to the wide spectrum of traffic/road structure scenarios and the long …
driving, However, due to the wide spectrum of traffic/road structure scenarios and the long …
Optimizing Traffic Signal Control in Mixed Traffic Scenarios: A Predictive Traffic Information-Based Deep Reinforcement Learning Approach
Z Zhang, B Zhou, B Zhang, P Cheng… - 2024 Forum for …, 2024 - ieeexplore.ieee.org
The rapid advancement of Connected Autonomous Vehicles (CAVs) is a driving force in the
evolution of smart cities and Intelligent Transportation Systems (ITS). This has spurred …
evolution of smart cities and Intelligent Transportation Systems (ITS). This has spurred …
Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …
vehicles plan future trajectories. We develop a partially observable Markov decision process …
Parameter sharing reinforcement learning for modeling multi-agent driving behavior in roundabout scenarios
F Konstantinidis, M Sackmann… - 2021 IEEE …, 2021 - ieeexplore.ieee.org
Modeling other drivers' behavior in highly interactive traffic situations, such as roundabouts,
is a challenging task. We address this task using a Multi-Agent Reinforcement Learning …
is a challenging task. We address this task using a Multi-Agent Reinforcement Learning …