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.

ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots / Shen, X.; Batkovic, I.; Govindarajan, V.; Falcone, P.; Darrell, T.; Borrelli, F.. - (2020), pp. 1170-1175. (Intervento presentato al convegno 31st IEEE Intelligent Vehicles Symposium, IV 2020 tenutosi a usa nel 2020) [10.1109/IV47402.2020.9304795].

ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots

Falcone P.;
2020

Abstract

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.
2020
31st IEEE Intelligent Vehicles Symposium, IV 2020
usa
2020
1170
1175
Shen, X.; Batkovic, I.; Govindarajan, V.; Falcone, P.; Darrell, T.; Borrelli, F.
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots / Shen, X.; Batkovic, I.; Govindarajan, V.; Falcone, P.; Darrell, T.; Borrelli, F.. - (2020), pp. 1170-1175. (Intervento presentato al convegno 31st IEEE Intelligent Vehicles Symposium, IV 2020 tenutosi a usa nel 2020) [10.1109/IV47402.2020.9304795].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1230748
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