Probabilistic trajectory prediction with Gaussian mixture models

By Jürgen Wiest, M. Höffken, U. Kressel, Klaus C. J. Dietmayer. IEEE Intelligent Vehicles Symposium, 2012

337 citations recorded by the source corpus.

Subfields: Automotive Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/1aba16bf1a3dd674611cbe48110ee6a56f7eda16

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In Automotive Engineering

In Safety, Risk, Reliability and Quality

Citations in the corpus

Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs (2018, background): “This can be addressed by data-driven approaches to trajectory prediction [5], [10]–[12].”
Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis (2013, methodology): “Depiction of trajectory prediction, aiming to map the most likely future vehicle motion, based on observed motion [185].”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “3) Trajectories: In [163], a long-term prediction method of vehicles was proposed.”
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture (2018, methodology): “In order to overcome the limitation, more sophisticated trajectory models were introduced, including Gaussian process model [3], [4], Gaussian mixture model [5], and dynamic Bayesian network (DBN) [6].”
Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network (2017, methodology): “The vehicle trajectory generating model has been learned through Gaussian process [5], [6] and Gaussian mixture model [7].”
How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction (2018, methodology): “An alternative is offered by probabilistic trajectory prediction approaches [12]–[17] that learn typical motion patterns of traffic from a trajectory dataset.”
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles (2020, background): “Different techniques exist to mitigate these effects by predicting the possible trajectories of other road users, like in [6], where the authors used gaussian mixture models, or in [7], where support vector machines and artiﬁcial neural networks were trained based on recorded trafﬁc data.”
Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks (2018, methodology): “Simulations [9], or Variational Gaussian Mixture Models [10].”