  Autonomous vehicles require accurate and reliable short-term trajectory
predictions for safe and efficient driving. While most commercial automated
vehicles currently use state machine-based algorithms for trajectory
forecasting, recent efforts have focused on end-to-end data-driven systems.
Often, the design of these models is limited by the availability of datasets,
which are typically restricted to generic scenarios. To address this
limitation, we have developed a synthetic dataset for short-term trajectory
prediction tasks using the CARLA simulator. This dataset is extensive and
incorporates what is considered complex scenarios - pedestrians crossing the
road, vehicles overtaking - and comprises 6000 perspective view images with
corresponding IMU and odometry information for each frame. Furthermore, an
end-to-end short-term trajectory prediction model using convolutional neural
networks (CNN) and long short-term memory (LSTM) networks has also been
developed. This model can handle corner cases, such as slowing down near zebra
crossings and stopping when pedestrians cross the road, without the need for
explicit encoding of the surrounding environment. In an effort to accelerate
this research and assist others, we are releasing our dataset and model to the
research community. Our datasets are publicly available on
https://github.com/sharmasushil/Navigating-Uncertainty-Trajectory-Prediction .
