Search Results for author:
Found 22 papers, 4 papers with code
Correctness Learning: Deductive Verification Guided Learning for Human-AI Collaboration
Despite significant progress in AI and decision-making technologies in safety-critical fields, challenges remain in verifying the correctness of decision output schemes and verification-result driven design.
Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach
The stability analysis result shows that the physical parameters in APeCG is able to reproduce the platoon stability in real-world condition.
Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model
To guarantee the safety and reliability of autonomous vehicle (AV) systems, corner cases play a crucial role in exploring the system's behavior under rare and challenging conditions within simulation environments.
Few-Shot Testing of Autonomous Vehicles with Scenario Similarity Learning
This severely hinders the testing and evaluation process, especially for third-party testers and governmental bodies with very limited testing budgets.
Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles
A method that facilitates easily controllable scenario generation for efficient autonomous vehicles (AV) testing with realistic and challenging situations is greatly needed.
Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems
The complexity of predicting criticality arises from the extreme data imbalance caused by rare events in high dimensional variables associated with the rare events, a challenge we refer to as the curse of rarity.
Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning
A common approach involves designing testing scenarios based on prior knowledge of CAVs (e. g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs' safety performances.
Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity
To alleviate the considerable uncertainty inherent in a small testing scenario set, we frame the FST problem as an optimization problem and search for the testing scenario set based on neighborhood coverage and similarity.
Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Distant Supervision
To address this problem, we take advantage of the memorization effects of deep neural networks and a small amount of annotated data to get a model with much knowledge and a little noise, and then we use this model to relabel the ancient Chinese sentences in parallel corpus.
Adaptive Safety Evaluation for Connected and Automated Vehicles with Sparse Control Variates
One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances.
Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios
To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times.
Exploring Contextual Relationships for Cervical Abnormal Cell Detection
To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection.
Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm
The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model prediction.
Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents
This method uses distillation to effectively avoid the weakness of STBP, which can achieve SOTA performance in classification, and can obtain a smaller, faster convergence and lower power consumption SNN reinforcement learning model.
Optimal Cooperative Driving at Signal-Free Intersections with Polynomial-Time Complexity
Cooperative driving at signal-free intersections, which aims to improve driving safety and efficiency for connected and automated vehicles, has attracted increasing interest in recent years.
Distributed Cooperative Driving in Multi-Intersection Road Networks
Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years.
Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles
In this paper, a unified framework is proposed to generate corner cases for the decision-making systems.
A Learning-based Stochastic Driving Model for Autonomous Vehicle Testing
However, pre-determined BV trajectories can not react to the AV's maneuvers, and deterministic models are different from real human drivers due to the lack of stochastic components and errors.
Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing
Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs).
Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework
A customized testing scenario library for a specific CAV model is generated through an adaptive process.
Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies
In Part I of this study, a general methodology for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation.
Cognitive Internet of Things: A New Paradigm beyond Connection
Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations.
