MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction
To predict future trajectories, the normalizing flow with a standard Gaussian prior suffers from weak diversity. The ineffectiveness comes from the conflict between the fact of asymmetric and multi-modal distribution of likely outcomes and symmetric and single-modal original distribution and supervision losses. Instead, we propose constructing a mixed Gaussian prior for a normalizing flow model for trajectory prediction. The prior is constructed by analyzing the trajectory patterns in the training samples without requiring extra annotations while showing better expressiveness and being multi-modal and asymmetric. Besides diversity, it also provides better controllability for probabilistic trajectory generation. We name our method Mixed Gaussian Flow (MGF). It achieves state-of-the-art performance in the evaluation of both trajectory alignment and diversity on the popular UCY/ETH and SDD datasets. Code is available at https://github.com/mulplue/MGF.
Code (1)
Tasks
DiversityPredictionTrajectory PredictionSimilar Papers 제목 키워드 기반
OnSiteVRU: A High-Resolution Trajectory Dataset for High-Density Vulnerable Road Users
With the acceleration of urbanization and the growth of transportation demands, the safety of vulnerable road users (VRUs, such as pedestrians and cyclists) in mixed traffic flows has become increasingly prominent, neces…
Autonomous DrivingTrajectory PredictionBayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
Dynamical modelling is central to many scientific domains, including pharmacometrics, systems biology, physiology, and epidemiology. In these settings, heterogeneity is often intrinsic: different subjects or units follow…
Trajectory PredictionGaussian ProcessesFast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction
Safety-critical applications such as autonomous vehicles and social robots require fast computation and accurate probability density estimation on trajectory prediction. To address both requirements, this paper presents …
Autonomous VehiclesDensity EstimationPredictionTrajectory PredictionA Universal Model for Human Mobility Prediction
Predicting human mobility is crucial for urban planning, traffic control, and emergency response. Mobility behaviors can be categorized into individual and collective, and these behaviors are recorded by diverse mobility…
modelPredictionMitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control
The use of connected automated vehicle (CAV) is advocated to mitigate traffic oscillations in mixed traffic flow consisting of CAVs and human driven vehicles (HDVs). This study proposes an adaptive deep Koopman predictiv…
Model Predictive ControlTrajectory Prediction