Sharpening the Future of Occupancy Grid Map Prediction Methods

An Investigation into Loss Functions and Semantic Segmentation Multi-Task learning for More Accurate OGM Predictions

Master thesis (2022)

Authors

R.F. Dirks Mechanical Engineering

Contributors

H.J. Boekema Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (supervisor 1)
D. Gavrila Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (supervisor 2)
E.A.I. Pool Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (supervisor 2)
J.F.P. Kooij Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (coach)
Faculty
Mechanical Engineering
Copyright: © 2022 Rutger Dirks
Deep Learning Semantic Segmentation Multi-task learning Loss Functions Motion Prediction Occupancy Grid Map Sinkhorn Loss Structural Similarity Index Measure
More Info
expand_more

Published Date

13-06-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical Engineering

Abstract

For an Autonomous Vehicle (AV) to traverse safely in traffic, It is vital it can anticipate the behavior of surrounding traffic participants using motion prediction. Current motion prediction approaches can be categorized into object-centered and object-agnostic methods and are primarily based on deep learning. The former relies on a human-engineered pipeline of object detection and tracking, of which the errors can accumulate in the motion predictions. The latter does not rely on this pipeline; however, it lacks the ability to learn object representations causing blurriness and object disappearances for longer-term predictions which forms a safety hazard. This thesis proposes two methods to improve the performance of the object-agnostic sequence-to-sequence Occupancy Grid Map (OGM) prediction networks, trained on the Waymo Open Perception dataset. The first method uses inter-pixel loss functions, i.e. the SSIM and Sinkhorn losses, instead of the ubiquitously used per-pixel losses, to train the PredRNN++ Occupancy Grid Map (OGM) prediction network. Inter-pixel losses take into account the spatial relations between grid cells during the evaluation of OGMs, whereas per-pixel losses evaluate each grid cell’s value independently. The quantitative results demonstrate that using inter-pixel losses can improve short term predictions with a prediction horizon of T = 5 for the Mean Squared Error (MSE) by 4.3%, Image Similarity (IS) by 7.8%, Average Precision (AP) by 0.3%, metrics. For the longer term, T = 15, the predictions improve for the MSE by 5.0%, IS by 20.5%, AP by 0.1%, and Accuracy by 0.6%. Furthermore, the use of inter-pixel losses reduces blurriness and object disappearances. The second method is based on multi-task learning. By training the PredRNN++ to perform the prediction task together with the semantic segmentation task on the predicted OGMs, it is expected to learn object representations which it uses to improve the prediction quality. The quantitative results show that multi-task learning does not improve the OGM predictions. However, some qualitative results show that multi-task learning reduces blurriness and object disappearances.