Prognostication of vehicle trajectories in unknown environments is intrinsically a challenging and difficult problem to solve. The behavior of such vehicles is highly influenced by surrounding traffic, road conditions, and rogue participants present in the environment. Moreover, the presence of pedestrians, traffic lights, stop signs, etc., makes it much harder to infer the behavior of various traffic agents. This paper attempts to solve the problem of Spatio-temporal look-ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network. The Memory Neuron Network (MNN) attempts to capture the input-output relationship between the past positions and the future positions of the traffic agents. The proposed model is computationally less intensive and has a simple architecture as compared to other deep learning models that utilize LSTMs and GRUs. It is then evaluated on the publicly available NGSIM dataset and its performance is compared with several state-of-art algorithms. Additionally, the performance is also evaluated on a custom synthetic dataset generated from the CARLA simulator. It is seen that the proposed model outperforms the existing state-of-art algorithms. Finally, the model is integrated with the CARLA simulator to test its robustness in real-time traffic scenarios.

中文翻译：

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基于记忆神经网络的时空超前轨迹预测

在未知环境中对车辆轨迹进行预测本质上是一个具有挑战性和困难的问题。此类车辆的行为在很大程度上受到周围交通，道路状况以及环境中存在的流氓​​参与者的影响。此外，行人，交通信号灯，停车标志等的出现使得推断各种交通代理的行为变得更加困难。本文试图使用一种称为记忆神经元网络的新型递归神经网络解决时空时空超前轨迹预测的问题。记忆神经元网络（MNN）试图捕获流量代理的过去位置和将来位置之间的输入-输出关系。与使用LSTM和GRU的其他深度学习模型相比，该模型的计算强度较低，并且具有简单的体系结构。然后在可公开获取的NGSIM数据集上对其进行评估，并将其性能与几种最新算法进行比较。此外，还可以根据从CARLA模拟器生成的自定义合成数据集来评估性能。可以看出，所提出的模型优于现有的最新算法。最后，该模型与CARLA仿真器集成在一起，可以在实时交通情况下测试其健壮性。还可以从CARLA模拟器生成的自定义合成数据集上评估性能。可以看出，所提出的模型优于现有的最新算法。最后，该模型与CARLA仿真器集成在一起，可以在实时交通情况下测试其健壮性。还可以从CARLA模拟器生成的自定义合成数据集上评估性能。可以看出，所提出的模型优于现有的最新算法。最后，该模型与CARLA仿真器集成在一起，可以在实时交通情况下测试其健壮性。