  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.
