Data-driven Traffic Simulation: A Comprehensive Review
Autonomous vehicles (AVs) have the potential to significantly revolutionize society by
providing a secure and efficient mode of transportation. Recent years have witnessed …
providing a secure and efficient mode of transportation. Recent years have witnessed …
A reinforcement learning method based on an improved sampling mechanism for unmanned aerial vehicle penetration
Y Wang, K Li, X Zhuang, X Liu, H Li - Aerospace, 2023 - mdpi.com
The penetration of unmanned aerial vehicles (UAVs) is an important aspect of UAV games.
In recent years, UAV penetration has generally been solved using artificial intelligence …
In recent years, UAV penetration has generally been solved using artificial intelligence …
Modeling human road crossing decisions as reward maximization with visual perception limitations
Y Wang, AR Srinivasan, JPP Jokinen… - 2023 IEEE Intelligent …, 2023 - ieeexplore.ieee.org
Understanding the interaction between different road users is critical for road safety and
automated vehicles (AVs). Existing mathematical models on this topic have been proposed …
automated vehicles (AVs). Existing mathematical models on this topic have been proposed …
A Penetration Method for UAV Based on Distributed Reinforcement Learning and Demonstrations
K Li, Y Wang, X Zhuang, H Yin, X Liu, H Li - Drones, 2023 - mdpi.com
The penetration of unmanned aerial vehicles (UAVs) is an essential and important link in
modern warfare. Enhancing UAV's ability of autonomous penetration through machine …
modern warfare. Enhancing UAV's ability of autonomous penetration through machine …
Predicting Driver Behavior on the Highway with Multi-Agent Adversarial Inverse Reinforcement Learning
H Radtke, H Bey, M Sackmann… - 2023 IEEE Intelligent …, 2023 - ieeexplore.ieee.org
For the implementation of autonomous or highly automated driving functions, predicting the
driver behavior of the surrounding road users is highly relevant. This work investigates the …
driver behavior of the surrounding road users is highly relevant. This work investigates the …
Fast Long-Term Multi-Scenario Prediction for Maneuver Planning at Unsignalized Intersections
MB Mertens, J Ruof, J Strohbeck… - arXiv preprint arXiv …, 2024 - arxiv.org
Motion prediction for intelligent vehicles typically focuses on estimating the most probable
future evolutions of a traffic scenario. Estimating the gap acceptance, ie, whether a vehicle …
future evolutions of a traffic scenario. Estimating the gap acceptance, ie, whether a vehicle …
Decision Making for Driving Agent in Traffic Simulation via Adversarial Inverse Reinforcement Learning
N Zhong, J Chen, Y Ma, W Jiang - 2023 IEEE 26th International …, 2023 - ieeexplore.ieee.org
Traffic simulation has the potential to facilitate the development and testing of autonomous
vehicles, as a supplement to road testing. Since autonomous vehicles will coexist with …
vehicles, as a supplement to road testing. Since autonomous vehicles will coexist with …
Reinforcement Learning-Based Navigation Approach for a Downscaled Autonomous Vehicle in Simplified Urban Scenarios
This paper presents the implementation of a reinforcement learning based navigation
architecture for autonomous vehicles in urban scenarios. These types of scenarios represent …
architecture for autonomous vehicles in urban scenarios. These types of scenarios represent …