Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
Rapid advances in Machine Learning (ML) have triggered new trends in Autonomous
Vehicles (AVs). ML algorithms play a crucial role in interpreting sensor data, predicting …
Vehicles (AVs). ML algorithms play a crucial role in interpreting sensor data, predicting …
A data-driven solution for intelligent power allocation of connected hybrid electric vehicles inspired by offline deep reinforcement learning in V2X scenario
Z Niu, H He - Applied Energy, 2024 - Elsevier
The proper power allocation between multiple energy sources is crucial for hybrid electric
vehicles to guarantee energy economy. As a data-driven technique, offline deep …
vehicles to guarantee energy economy. As a data-driven technique, offline deep …
A robust operators' cognitive workload recognition method based on denoising masked autoencoder
Identifying the cognitive workload of operators is crucial in complex human-automation
collaboration systems. An excessive workload can lead to fatigue or accidents, while an …
collaboration systems. An excessive workload can lead to fatigue or accidents, while an …
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) …
Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations
Despite the substantial advancements in reinforcement learning (RL) in recent years,
ensuring trustworthiness remains a formidable challenge when applying this technology to …
ensuring trustworthiness remains a formidable challenge when applying this technology to …
Deep reinforcement learning-based off-road path planning via low-dimensional simulation
X Wang, E Shang, B Dai, Y Nie… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Path planning is a critical aspect of autonomous driving. Traditional methods have excelled
in urban environments but struggle off-road due to unpredictable conditions. This paper …
in urban environments but struggle off-road due to unpredictable conditions. This paper …
Ensembled Traffic-Aware Transformer-Based Predictive Energy Management for Electrified Vehicles
The predictive energy management strategy (PEMS) offers potential advantages in
enhancing the driving economy of electrified vehicles using vehicle speed prediction …
enhancing the driving economy of electrified vehicles using vehicle speed prediction …
Utilizing a diffusion model for pedestrian trajectory prediction in semi-open autonomous driving environments
In recent years, the pervasive deployment and progression of autonomous driving
technology have engendered heightened demands, particularly within the intricate campus …
technology have engendered heightened demands, particularly within the intricate campus …
Transformer-based traffic-aware predictive energy management of a fuel cell electric vehicle
The energy economy of fuel cell electric vehicles (FCEVs) plays a crucial role in determining
their practicality, making the optimization of energy management strategies (EMS) essential …
their practicality, making the optimization of energy management strategies (EMS) essential …
An explainable and robust motion planning and control approach for autonomous vehicle on-ramping merging task using deep reinforcement learning
B Hu, L Jiang, S Zhang, Q Wang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Reinforcement learning (RL) has the capability to discover optimal interactions with the
surrounding environment, with the advantage that nearly all required computations can be …
surrounding environment, with the advantage that nearly all required computations can be …