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UID:event-56@tuemeche.nl
DTSTAMP:20261007T234623Z
DTSTART;TZID=Europe/Amsterdam:20261019T140000
DTEND;TZID=Europe/Amsterdam:20261019T163000
SUMMARY:Ego Trajectory Prediction for Autonomous Vehicles Using a Dynamic
 s-Residual Hybrid Model
DESCRIPTION:Speaker: Walter Nijhout\nHost: Tom van der Sande\n\nThis work
  presents a hybrid trajectory prediction model for autonomous buses\, in 
 a yard-manoeuvring use case (<15 km/h). A hybrid residual model (HM) is u
 sed\, where an analytical model (AM) is combined with a data-driven model
  (DM)\, in this case an LSTM. In this method\, the AM provides an initial
  estimate of the bus's yaw rate and lateral velocity\, and a DM learns to
  correct the residual error of that estimate. A high-fidelity multi-body 
 simulation is used as ground truth for training\, where it is excited usi
 ng a random-phase multisine. The HM's objective function includes both ve
 locities and position\, thereby encouraging both physically plausible and
  accurate trajectories. Additionally\, two differing complexities of AMs 
 are used to investigate whether a more complex AM lets the HM predict tra
 jectories more accurately. Over 10 separate training runs\, numerical res
 ults show that the HM is 94 % more accurate than both baseline AMs in an 
 unseen test-set\, and that the different complexities in AMs do not make 
 a significant difference in absolute accuracy. The HM using the more comp
 lex AM is found to have larger training-validation gap compared to the HM
  with the less complex AM.
LOCATION:Pendulum 3.26
URL:https://tuemeche.nl/peoplepages/event.php?id=56
CATEGORIES:MSc Thesis Defense
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