ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots

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ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots

Shen, Xu / Batkovic, Ivo / Govindarajan, Vijay / Falcone, Paolo / Darrell, Trevor / Borrelli, Francesco

We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space. Using the CARLA simulator, we develop a parking lot environment and collect a dataset of human parking maneuvers. We then study the impact of model complexity and feature information by comparing a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment improves long term predictions.

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Dokumentinformationen

Titel :

ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots

Beteiligte:

Shen, Xu (Autor:in) / Batkovic, Ivo (Autor:in) / Govindarajan, Vijay (Autor:in) / Falcone, Paolo (Autor:in) / Darrell, Trevor (Autor:in) / Borrelli, Francesco (Autor:in)

Erschienen in:

2020 IEEE Intelligent Vehicles Symposium (IV) ; 1170-1175

Erscheinungsdatum :

2020-10-19

Format / Umfang :

2374155 byte

ISBN :

978-1-7281-6673-5 , 978-1-7281-6672-8

ISSN :

2642-7214

DOI :

https://doi.org/10.1109/IV47402.2020.9304795