Deep reinforcement learning with predictive auxiliary task for autonomous train collision avoidance
A Plissonneau, L Jourdan, D Trentesaux, L Abdi… - Journal of Rail Transport …, 2024 - Elsevier
The contribution of this paper consists of a deep reinforcement learning (DRL) based
method for autonomous train collision avoidance. While DRL applied to autonomous …
method for autonomous train collision avoidance. While DRL applied to autonomous …
Deep reinforcement learning based game-theoretic decision-making for autonomous vehicles
This letter presents an approach for implementing game-theoretic decision-making in
combination with deep reinforcement learning to allow vehicles to make decisions at an …
combination with deep reinforcement learning to allow vehicles to make decisions at an …
Adaptive decision-making for automated vehicles under roundabout scenarios using optimization embedded reinforcement learning
The roundabout is a typical changeable, interactive scenario in which automated vehicles
should make adaptive and safe decisions. In this article, an optimization embedded …
should make adaptive and safe decisions. In this article, an optimization embedded …
Integration of Decision-Making and Motion Planning for Autonomous Driving Based on Double-Layer Reinforcement Learning Framework
Autonomous driving involves multi-timescale and multi-objective tasks coupled with long-
term driving decision and short-term motion planning. However, existing studies tend to …
term driving decision and short-term motion planning. However, existing studies tend to …
Integrating Expert Guidance for Efficient Learning of Safe Overtaking in Autonomous Driving Using Deep Reinforcement Learning
Overtaking on two-lane roads is a great challenge for autonomous vehicles, as oncoming
traffic appearing on the opposite lane may require the vehicle to change its decision and …
traffic appearing on the opposite lane may require the vehicle to change its decision and …
Decision-making strategy on highway for autonomous vehicles using deep reinforcement learning
Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
Continuous decision making for on-road autonomous driving under uncertain and interactive environments
Although autonomous driving techniques have achieved great improvements, challenges
still exist in decision making for variety of different scenarios under uncertain and interactive …
still exist in decision making for variety of different scenarios under uncertain and interactive …
Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles
Reinforcement learning (RL) has demonstrated considerable potential in solving challenges
across various domains, notably in autonomous driving. Nevertheless, implementing RL in …
across various domains, notably in autonomous driving. Nevertheless, implementing RL in …
An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning
Y Liu, S Diao - PLoS one, 2024 - journals.plos.org
As autonomous driving technology continues to advance and gradually become a reality,
ensuring the safety of autonomous driving in complex traffic scenarios has become a key …
ensuring the safety of autonomous driving in complex traffic scenarios has become a key …
Deductive reinforcement learning for visual autonomous urban driving navigation
Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …