Reinforcement learning for autonomous driving with latent state inference and spatial-temporal relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …
complex autonomous driving scenarios. However, identifying the subtle cues that can …
Learning to drive at unsignalized intersections using attention-based deep reinforcement learning
H Seong, C Jung, S Lee… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Driving at an unsignalized intersection is a complex traffic scenario that requires both traffic
safety and efficiency. At the unsignalized intersection, the driving policy does not simply …
safety and efficiency. At the unsignalized intersection, the driving policy does not simply …
Dynamic interaction-aware scene understanding for reinforcement learning in autonomous driving
The common pipeline in autonomous driving systems is highly modular and includes a
perception component which extracts lists of surrounding objects and passes these lists to a …
perception component which extracts lists of surrounding objects and passes these lists to a …
Model-free deep reinforcement learning for urban autonomous driving
Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …
and multi-agent interactions. Current decision making methods are mostly manually …
Deep reinforcement learning framework for autonomous driving
Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …
teach machines through interaction with the environment and learning from their mistakes …
Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation
Enabling robots to autonomously navigate complex environments is essential for real-world
deployment. Prior methods approach this problem by having the robot maintain an internal …
deployment. Prior methods approach this problem by having the robot maintain an internal …
A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles
In this survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
Integrating deep reinforcement learning with model-based path planners for automated driving
Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
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. This is because the obstacle …
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …