We present a Long Short Term Memory (LSTM) encoder-decoder architecture to anticipate the future positions of vehicles in a road network given several seconds of historical observations and associated map features. Unlike existing architectures, the proposed method incorporates and updates the surrounding vehicle information in both the encoder and decoder, making use of dynamically predicted new data for accurate prediction in longer time horizons. It seamlessly performs four tasks: the first task encodes a feature given the past observations, the second task estimates future maneuvers given the encoded state, the third task predicts the future motion given the estimated maneuvers and the initially encoded states, and the fourth task estimates future trajectory given the encoded state and the predicted maneuvers and motions. Experiments on a public benchmark and a new, publicly available radar dataset demonstrate that our approach can equal or surpass the state-of-the-art for long term trajectory prediction.

中文翻译：

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使用汽车雷达和循环神经网络预测车辆行为


我们提出了一种长短期记忆（LSTM）编码器-解码器架构，可以根据几秒钟的历史观察结果和相关的地图特征来预测道路网络中车辆的未来位置。与现有架构不同，所提出的方法在编码器和解码器中合并并更新周围车辆信息，利用动态预测的新数据在较长时间范围内进行准确预测。它无缝地执行四个任务：第一个任务对给定过去观察的特征进行编码，第二个任务在给定编码状态的情况下估计未来的动作，第三个任务在给定估计的动作和初始编码状态的情况下预测未来的运动，第四个任务估计给定编码状态和预测的机动和运动的未来轨迹。对公共基准和新的公开雷达数据集的实验表明，我们的方法可以等于或超过长期轨迹预测的最先进技术。