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 …
A survey on path planning for autonomous ground vehicles in unstructured environments
N Wang, X Li, K Zhang, J Wang, D Xie - Machines, 2024 - mdpi.com
Autonomous driving in unstructured environments is crucial for various applications,
including agriculture, military, and mining. However, research in unstructured environments …
including agriculture, military, and mining. However, research in unstructured environments …
Robust Multiobjective Reinforcement Learning Considering Environmental Uncertainties
X He, J Hao, X Chen, J Wang, X Ji… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Numerous real-world decision or control problems involve multiple conflicting objectives
whose relative importance (preference) is required to be weighed in different scenarios …
whose relative importance (preference) is required to be weighed in different scenarios …
HGRL: Human-Driving-Data Guided Reinforcement Learning for Autonomous Driving
H Zhuang, H Chu, Y Wang, B Gao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …
However, designing appropriate reward functions to guide RL agents towards complex …
Subjective Scoring Framework for VQA Models in Autonomous Driving
The development of vision and language transformer models has paved the way for Visual
Question Answering (VQA) models and related research. There are metrics to assess the …
Question Answering (VQA) models and related research. There are metrics to assess the …
Path Tracking Control Based on TS Fuzzy Model for Autonomous Vehicles with Yaw Angle and Heading Angle
Y He, J Wu, F Xu, X Liu, S Wang, G Cui - Machines, 2024 - mdpi.com
Existing vehicle-road models used for road tracking do not take into account the side slip
angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is …
angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is …
Adaptive Kalman-based hybrid car following strategy using TD3 and CACC
In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative
adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and …
adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and …