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
Location
Bosch Group Hungary
104 Gyömrői út, Budapest, 1103
Authors
Quantitative Researcher
I am a PhD researcher in Mathematics with experience in stochastic modeling, probabilistic analysis, and large-scale simulation, supported by Python/C++ model development.
Previously, I worked as a machine learning researcher at Bosch, where I developed and validated predictive models with a focus on uncertainty estimation and data-driven decision-making.
I am interested in applying quantitative methods to forecasting and risk modeling in energy and financial markets.