Motion planning for autonomous driving: The state of the art and future perspectives
Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …
convenience, safety advantages, and potential commercial value. Despite predictions of …
Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives
H He, X Meng, Y Wang, A Khajepour, X An… - … and Sustainable Energy …, 2024 - Elsevier
Electrified vehicles provide an effective solution to address the unfavorable impacts of fossil
fuel use in the transportation sector. Energy management strategy (EMS) is the core …
fuel use in the transportation sector. Energy management strategy (EMS) is the core …
[HTML][HTML] Toward human-in-the-loop AI: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving
Due to its limited intelligence and abilities, machine learning is currently unable to handle
various situations thus cannot completely replace humans in real-world applications …
various situations thus cannot completely replace humans in real-world applications …
Uncertainty-aware model-based reinforcement learning: Methodology and application in autonomous driving
To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …
Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving
Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
Multi-modal motion prediction with transformer-based neural network for autonomous driving
Predicting the behaviors of other agents on the road is critical for autonomous driving to
ensure safety and efficiency. However, the challenging part is how to represent the social …
ensure safety and efficiency. However, the challenging part is how to represent the social …
Towards robust decision-making for autonomous driving on highway
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
Deep learning technology for construction machinery and robotics
K You, C Zhou, L Ding - Automation in construction, 2023 - Elsevier
Construction machinery and robots are essential equipment for major infrastructure. The
application of deep learning technology can improve the construction quality and alleviate …
application of deep learning technology can improve the construction quality and alleviate …
Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …
systems, and learning-based behavior planning presents a promising pathway toward …
Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …
applications, but limited computing resource makes it challenging to deploy a well-behaved …