ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
Paper i proceeding, 2020

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.


Xu Shen

University of California

Ivo Batkovic

Chalmers, Elektroteknik, System- och reglerteknik

Zenuity AB

Vijay Govindarajan

University of California

Paolo Falcone

Chalmers, Elektroteknik, System- och reglerteknik

Trevor Darrell

University of California

Francesco Borrelli

University of California

IEEE Intelligent Vehicles Symposium, Proceedings

1170-1175 9304795

31st IEEE Intelligent Vehicles Symposium, IV 2020
Virtual, Las Vegas, USA,


Bioinformatik (beräkningsbiologi)

Robotteknik och automation

Datavetenskap (datalogi)



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