End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
A review of end-to-end autonomous driving in urban environments
D Coelho, M Oliveira - IEEE Access, 2022 - ieeexplore.ieee.org
Autonomous driving in urban environments requires intelligent systems that are able to deal
with complex and unpredictable scenarios. Traditional modular approaches focus on …
with complex and unpredictable scenarios. Traditional modular approaches focus on …
Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods
With extensive pre-trained knowledge and high-level general capabilities, large language
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …
Cadre: A cascade deep reinforcement learning framework for vision-based autonomous urban driving
Vision-based autonomous urban driving in dense traffic is quite challenging due to the
complicated urban environment and the dynamics of the driving behaviors. Widely-applied …
complicated urban environment and the dynamics of the driving behaviors. Widely-applied …
Lexicographic actor-critic deep reinforcement learning for urban autonomous driving
Urban autonomous driving is a difficult task because of its complex road scenarios and the
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …
Self-imitation guided goal-conditioned reinforcement learning
Goal-conditioned reinforcement learning (GCRL) aims to control agents to reach desired
goals, which poses a significant challenge due to task-specific variations in configurations …
goals, which poses a significant challenge due to task-specific variations in configurations …
AHEGC: Adaptive Hindsight Experience Replay With Goal-Amended Curiosity Module for Robot Control
With shaped reward functions, reinforcement learning (RL) has recently been successfully
applied to several robot control tasks. However, designing a task-relevant and well …
applied to several robot control tasks. However, designing a task-relevant and well …
RDDRL: a recurrent deduction deep reinforcement learning model for multimodal vision-robot navigation
Z Li, A Zhou - Applied Intelligence, 2023 - Springer
Existing deep reinforcement learning-based mobile robot navigation relies largely on single-
modal visual perception to perform local-scale navigation. However, multimodal visual …
modal visual perception to perform local-scale navigation. However, multimodal visual …
Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
Alleviating the estimation bias of deep deterministic policy gradient via co-regularization
Abstract The overestimation in Deep Deterministic Policy Gradients (DDPG) caused by
value approximation error may result in unstable policy training. Twin Delayed Deep …
value approximation error may result in unstable policy training. Twin Delayed Deep …