Crafted vs. Learned Representations in Predictive Models - A Case Study on Cyclist Path Prediction

Crafted vs. Learned Representations in Predictive Models - A Case Study on Cyclist Path Prediction

Pool, E.A.I. (TU Delft Intelligent Vehicles)
Kooij, J.F.P. (TU Delft Intelligent Vehicles)
Gavrila, D. (TU Delft Intelligent Vehicles)

2021

This paper compares two models for context-based path prediction of objects with switching dynamics: a Dynamic Bayesian Network (DBN) and a Recurrent Neural Network (RNN). These models are instances of two larger model categories, distinguished by whether expert knowledge is explicitly crafted into the state representation (and thus is interpretable) or whether the representation is learned from data, respectively. Both have shown state-of-the-art performance in previous work. In order to provide a fair comparison, we ensure that both models are treated similarly with respect to the use of context cues and parameter estimation. Specifically, we describe (1) how to integrate the context cues (used previously by the DBN) into the RNN, and (2) how to optimize the DBN with back-propagation similar to the RNN, while keeping an interpretable state representation. Experiments are performed on a scenario where a cyclist might turn left at an intersection in front of the ego-vehicle. Results show that the RNN successfully leverages the context cues, and that optimizing the DBN improves its performance with respect to existing work. While the RNN outperforms the optimized DBN in predictive log-likelihood by a significant margin, both models attain similar average Euclidean distance errors (23-39 cm for DBN and 31-34 cm for RNN, predicting 1 s ahead).

Active safety
Context modeling
Dynamics
Intelligent vehicles
motion prediction
Predictive models
Recurrent neural networks
Roads
Vehicle dynamics
vulnerable road users (VRUs)

http://resolver.tudelft.nl/uuid:b634c2db-227b-4b29-81ba-84c075a03bf8

https://doi.org/10.1109/TIV.2021.3064253

2021-09-08

2379-8858

IEEE Transactions on Intelligent Vehicles, 6 (4), 747-759

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2021 E.A.I. Pool, J.F.P. Kooij, D. Gavrila