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EVALUATION FRAMEWORK FOR PREDICTED TRAJECTORIES IN AUTONOMOUS DRIVING VEHICLE TRAFFIC PREDICTION

机译:自主驾驶汽车交通预测中的预测轨迹评估框架

摘要

According to one embodiment, when a predicted trajectory is received, a set of one or more features are extracted from at least some of the trajectory points of the predicted trajectory. The predicted trajectory is predicted using a prediction method or algorithm based on perception data perceiving an object within a driving environment surrounding an autonomous driving vehicle (ADV). The extracted features are fed into a predetermined DNN model to generate a similarity score. The similarity score represents a difference or similarity between the predicted trajectory and a prior actual trajectory that was used to train the DNN model. The similarity score can be utilized to evaluate the prediction method that predicted the predicted trajectory.
机译:根据一个实施例,当接收到预测轨迹时,从该预测轨迹的至少一些轨迹点中提取一组一个或多个特征。使用预测方法或算法基于感知数据来预测预测的轨迹,该感知数据感知在自动驾驶车辆(ADV)周围的驾驶环境内的物体。所提取的特征被馈送到预定的DNN模型中以生成相似度分数。相似度得分表示预测的轨迹与用于训练DNN模型的先前的实际轨迹之间的差异或相似性。相似度分数可用于评估对预测轨迹进行预测的预测方法。

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