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Found 15 papers, 5 papers with code
A Preprocessing and Evaluation Toolbox for Trajectory Prediction Research on the Drone Datasets
The availability of high-quality datasets is crucial for the development of behavior prediction algorithms in autonomous vehicles.
Neural Network-Based Piecewise Survival Models
In this paper, a family of neural network-based survival models is presented.
Usage-Specific Survival Modeling Based on Operational Data and Neural Networks
The results also show that randomly resampling the dataset on each epoch is an effective way to reduce the size of the training data.
Observer-Based Environment Robust Control Barrier Functions for Safety-critical Control with Dynamic Obstacles
This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with moving obstacles.
Diffusion-Based Environment-Aware Trajectory Prediction
The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles.
Structural Diagnosability Analysis of Switched and Modular Battery Packs
The findings indicate that the default sensor setup is insufficient for achieving complete fault isolability.
Stability-Informed Initialization of Neural Ordinary Differential Equations
This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques.
Uncertainties in Robust Planning and Control of Autonomous Tractor-Trailer Vehicles
To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed.
Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction
This research investigates the performance of various motion models in combination with numerical solvers for the prediction task.
Energy-Based Survival Models for Predictive Maintenance
Due to the complex behavior of system degradation, data-driven methods are often preferred, and neural network-based methods have been shown to perform particularly very well.
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior.
Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions
This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments.
Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data
In this work Time Series Classification techniques are investigated, and especially their applicability in applications where there are significant differences between the individuals where data is collected, and the individuals where the classification is evaluated.
Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences
Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training.
Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques
The use of learning-based methods for vehicle behavior prediction is a promising research topic.
