Interpretable Representation Learning for Motion Forecasting
We address interpretable representation learning for motion forecasting in self-driving cars. Rather than treating transformers as black boxes, we develop methods to interpret and modify learned representations. We introduce self-supervised pre-training with interpretable objectives. Moreover, we pr...
Sábháilte in:
| Príomhchruthaitheoir: | |
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| Formáid: | Online |
| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
KIT Scientific Publishing
2026
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20260519T105721_9783731514749_14 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869528448057212928 |
|---|---|
| author | Wagner, Royden |
| author_browse | Wagner, Royden |
| author_facet | Wagner, Royden |
| author_sort | Wagner, Royden |
| collection | Directory of Open Access Books |
| description | We address interpretable representation learning for motion forecasting in self-driving cars. Rather than treating transformers as black boxes, we develop methods to interpret and modify learned representations. We introduce self-supervised pre-training with interpretable objectives. Moreover, we probe latent spaces of forecasting models and reveal interpretable features, allowing us to make targeted interventions. Finally, we uncover retrocausal mechanisms, which enable goal-based instructions. |
| format | Online |
| id | doab-20.500.12854ir-176606 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1766062026-05-20T06:05:23Z Interpretable Representation Learning for Motion Forecasting Wagner, Royden Autonomes Fahren Bewegungsvorhersage Maschinelles Lernen Mechanistic interpretability Motion forecasting Representation learning Self-driving cars Transformer models thema EDItEUR::U Computing and Information Technology::UY Computer science We address interpretable representation learning for motion forecasting in self-driving cars. Rather than treating transformers as black boxes, we develop methods to interpret and modify learned representations. We introduce self-supervised pre-training with interpretable objectives. Moreover, we probe latent spaces of forecasting models and reveal interpretable features, allowing us to make targeted interventions. Finally, we uncover retrocausal mechanisms, which enable goal-based instructions. 2026-05-20T06:05:16Z 2026-05-20T06:05:16Z 2026-05-19T12:51:58Z 2026 book ONIX_20260519T105721_9783731514749_14 1613-4214 (Online) https://library.oapen.org/handle/20.500.12657/113239 9783731514749 https://directory.doabooks.org/handle/20.500.12854/176606 eng Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/113239/1/9783731514749.pdf KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000191275 10.5445/KSP/1000191275 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731514749 KIT Scientific Publishing 172 Karlsruhe, Germany open access |
| spellingShingle | Autonomes Fahren Bewegungsvorhersage Maschinelles Lernen Mechanistic interpretability Motion forecasting Representation learning Self-driving cars Transformer models thema EDItEUR::U Computing and Information Technology::UY Computer science Wagner, Royden Interpretable Representation Learning for Motion Forecasting |
| title | Interpretable Representation Learning for Motion Forecasting |
| title_full | Interpretable Representation Learning for Motion Forecasting |
| title_fullStr | Interpretable Representation Learning for Motion Forecasting |
| title_full_unstemmed | Interpretable Representation Learning for Motion Forecasting |
| title_short | Interpretable Representation Learning for Motion Forecasting |
| title_sort | interpretable representation learning for motion forecasting |
| topic | Autonomes Fahren Bewegungsvorhersage Maschinelles Lernen Mechanistic interpretability Motion forecasting Representation learning Self-driving cars Transformer models thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | Autonomes Fahren Bewegungsvorhersage Maschinelles Lernen Mechanistic interpretability Motion forecasting Representation learning Self-driving cars Transformer models thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20260519T105721_9783731514749_14 |
| work_keys_str_mv | AT wagnerroyden interpretablerepresentationlearningformotionforecasting |