駐車場における車両の運動と意図予測
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
CARLAシミュレータで駐車場環境と人間の駐車操作データセットを構築し、LSTMやCNN-LSTMが運転者の意図を高精度に推定でき、意図情報と環境の意味表現が駐車軌道予測を大きく改善することを示した。
著者: Xu Shen, Ivo Batkovic, Vijay Govindarajan, Paolo Falcone, Trevor Darrell, Francesco Borrelli
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
