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
2020 IEEE Intelligent Vehicles Symposium (IV) ; 1170-1175
2020-10-19
2374155 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
PARKPREDICT: MOTION AND INTENT PREDICTION OF VEHICLES IN PARKING LOTS
British Library Conference Proceedings | 2020
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