Talk - Adaptive Trajectory Prediction Based on Gaussian Stochastic Processes
Jul 14, 2020
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0 min read
Abstract
Trajectory prediction is an essential part of many algorithms in autonomous driving. Among its most important applications are behavior planning, collision mitigation, lane keeping and many others. By the stochastic modeling of dynamical parameters of motion the prediction of future trajectories becomes possible. In this document, a regression model using a stochastic approach is developed to successfully predict traffic participant trajectories, and equally important, estimate the prediction’s uncertainty. As the statistical properties of trajectory prediction depend on the traffic scene under consideration, the model is capable of adapting itself to the environment. The model was analyzed on a 400 km long measurement to verify its assumptions.
Date
Jul 14, 2020 2:00 PM — 2:30 PM
Event
Bosch Innovation Day
Location
Bosch Group Hungary
104 Gyömrői út, Budapest, 1103
Authors
Quantitative Researcher
Quantitative researcher with a PhD in Mathematics, combining expertise in stochastic modeling, statistical inference, and machine learning with industrial experience in developing predictive systems.
Experienced in large-scale time-series analysis, uncertainty quantification, and rigorous model validation, with strong Python/C++ skills and a proven track record of translating research into production software.
Currently developing intraday electricity-price models and stochastic-control methods for optimal liquidation at Aarhus University, in collaboration with an energy trading firm.