Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to assure safety while navigating through highly interactive complex scenarios. In this work, the framework IAMP (Interaction-Aware Motion Prediction), producing multi-modal probabilistic outputs from the integration of a Dynamic Bayesian Network and Markov Chains, is extended with a learning-based approach. The integration of a machine learning model tackles the limitations of the ruled-based mechanism since it can better adapt to different driving styles and driving situations. The method here introduced generates context-dependent acceleration distributions used in a Markov-chain-based motion prediction. This hybrid approach results in better evaluation metrics when compared with the baseline in the four highly-interactive scenarios obtained from publicly available datasets.
|Title of host publication||IV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - 2023|
|Event||34th IEEE Intelligent Vehicles Symposium, IV 2023 - Anchorage, United States|
Duration: 4 Jun 2023 → 7 Jun 2023
|Name||IEEE Intelligent Vehicles Symposium, Proceedings|
|Conference||34th IEEE Intelligent Vehicles Symposium, IV 2023|
|Period||4/06/23 → 7/06/23|
Bibliographical noteGreen 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
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