From the 1 of 16 linked papers with an AI index.
9 papers · 1 filter
Route by Kinematics, Act by Observation: Kinematics-Supervised Expert Routing in MoE-Augmented VLA
Tianhang Yang, Yanze Zheng, Junjie Wang +3
The paper introduces KinRT, a kinematics‑supervised routing method that clusters action trajectories to guide expert selection in mixture‑of‑experts vision‑language agents for robo…
FedDSR: Federated Deep Supervision and Regularization Towards Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng +3
Federated Learning (FL) enables collaborative training of autonomous driving (AD) models across distributed vehicles while preserving data privacy. However, FL encounters critical…
Statistic-Augmented, Decoupled MoE Routing and Aggregating in Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Jingreng Lei +3
Autonomous driving (AD) scenarios are inherently complex and diverse, posing significant challenges for a single deep learning model to effectively cover all possible conditions, s…
Adverse Weather-Independent Framework Towards Autonomous Driving Perception through Temporal Correlation and Unfolded Regularization
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye +5
Various adverse weather conditions such as fog and rain pose a significant challenge to autonomous driving (AD) perception tasks like semantic segmentation, object detection, etc.…
iMacHSR: Intermediate Multi-Access Heterogeneous Supervision and Regularization Scheme Toward Architecture-Agnostic Training
Wei-Bin Kou, Guangxu Zhu, Yichen Jin +4
While deep supervision is a powerful training strategy by supervising intermediate layers with auxiliary losses, it faces three underexplored problems: (I) Existing deep supervisio…
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng +3
Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization d…