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Found 9 papers, 1 papers with code
Estimating Causal Effects using a Multi-task Deep Ensemble
A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images.
Empirical Analysis of AI-based Energy Management in Electric Vehicles: A Case Study on Reinforcement Learning
The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function.
Potential Auto-driving Threat: Universal Rain-removal Attack
The problem of robustness in adverse weather conditions is considered a significant challenge for computer vision algorithms in the applicants of autonomous driving.
Progress and summary of reinforcement learning on energy management of MPS-EV
RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS.
A novel learning-based robust model predictive control energy management strategy for fuel cell electric vehicles
Next, the components including explicit data tables and vehicle velocity estimation are combined with model predictive control (MPC) to release the state-of-the-art energy-saving ability for the multi-freedom system in FCEVs, whose name is LRMPC.
Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model
Accurate traffic conditions prediction provides a solid foundation for vehicle-environment coordination and traffic control tasks.
Transfer Learning and Vision Transformer based State-of-Health prediction of Lithium-Ion Batteries
At the same time, transfer learning (TL) is introduced, and the prediction model based on source task battery training is further fine-tuned according to the early cycle data of the target task battery to provide an accurate prediction.
Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction
The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated.
A Transferable Intersection Reconstruction Network for Traffic Speed Prediction
Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models.
