Deep Learning-based Motion Prediction Leveraging Autonomous Driving Datasets: State-of-the-Art

Autonomous vehicles continue to advance, primarily due to the continuous development and improvement of deep learning methods. Motion prediction of road users is a function that humans perform naturally while driving, such as anticipating when a pedestrian is likely to cross an intersection or when a vehicle would like to merge into a lane. Similarly, the ability to predict the motion of other road users has become a key function for autonomous vehicles to optimally plan their path. This publication explores the recent advancements in motion prediction of road users propelled by both deep learning, as well as the release of large datasets. These datasets provide many challenging real-world scenarios which can be leveraged to train and test state-of-the-art deep learning models. This study provides an overview of motion prediction, state-of-the-art datasets, and state-of-the-art models along with a deep dive into their methods. Finally, there is a comparison of model performance, followed by recommendations into future research and concluding remarks.