论文标题
Parkpredict：动议和意图预测停车场的车辆
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
论文作者
论文摘要
我们研究了预测停车场驾驶员行为的问题，该环境的结构不如典型的道路网络，并且在紧凑的空间中具有复杂的互动式操纵。使用Carla模拟器，我们开发了一个停车场环境，并收集了人类停车手的数据集。然后，我们通过比较多模式的长期记忆（LSTM）预测模型和卷积神经网络LSTM（CNN-LSTM）与基于物理学的扩展Kalman滤波器（EKF）基线来研究模型复杂性和特征信息的影响。我们的结果表明，1）可以很好地估计意图（LSTM和CNN-LSTM模型的前1位准确性约为85％，前3个精度近100％）； 2）了解人类驾驶员预期的停车位对预测停车轨迹有重大影响； 3）环境的语义表示改善了长期预测。
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.
