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Found 17 papers, 1 papers with code
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians.
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
With this, we were able to train MGN on meshes with \textit{millions} of nodes to generate computational fluid dynamics (CFD) simulations.
Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation
To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents with different social characteristics in the simulation environment.
Analyzing and Enhancing Closed-loop Stability in Reactive Simulation
In this paper, we aim to provide a thorough stability analysis of the reactive simulation and propose a solution to enhance the stability.
Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation
Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems.
Transferable and Adaptable Driving Behavior Prediction
By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments.
Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction
With the feedback of the observed trajectory, the algorithm is applied to neural-network-based models to improve the performance of driving behavior predictions across different human subjects and scenarios.
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction
In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed.
Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction
Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles.
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving
The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method.
Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors
Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module.
Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory
Hence, the goal of this work is to formulate the human drivers' behavior generation model with CPT so that some ``irrational'' behavior or decisions of human can be better captured and predicted.
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios
In this paper, we proposed an interaction-aware decision making with adaptive strategies (IDAS) approach that can let the autonomous vehicle negotiate the road with other drivers by leveraging their cooperativeness under merging scenarios.
Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model
The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents.
A Framework for Probabilistic Generic Traffic Scene Prediction
In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles.
Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp.
Probabilistic Prediction of Vehicle Semantic Intention and Motion
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles.
