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Autonomous vehicles require accurate and reliable short-term trajectorypredictions for safe and efficient driving. While most commercial automatedvehicles currently use state machine-based algorithms for trajectoryforecasting, 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 thislimitation, we have developed a synthetic dataset for short-term trajectoryprediction tasks using the CARLA simulator. This dataset is extensive andincorporates what is considered complex scenarios - pedestrians crossing theroad, vehicles overtaking - and comprises 6000 perspective view images withcorresponding IMU and odometry information for each frame. Furthermore, anend-to-end short-term trajectory prediction model using convolutional neuralnetworks (CNN) and long short-term memory (LSTM) networks has also beendeveloped. This model can handle corner cases, such as slowing down near zebracrossings and stopping when pedestrians cross the road, without the need forexplicit encoding of the surrounding environment. In an effort to acceleratethis research and assist others, we are releasing our dataset and model to theresearch community. Our datasets are publicly available onhttps://github.com/sharmasushil/Navigating-Uncertainty-Trajectory-Prediction .

Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety