Pedestrian and vehicle behaviour prediction in autonomous vehicle system — A review.

Autonomous vehicles (AV)s have become a trending topic nowadays since they have the potential to solve traffic problems, such as accidents and congestion. Although AV systems have greatly evolved, it still have their limitations. For example, Google reported that their AVs have been involved in several collisions and near misses. While most of these collisions and near misses were caused by third parties, the AVs should be able to predict and avoid them. Events like this show that there is still room for improvement in the AV system. This paper aims to present a review of the state-of-the-art algorithms proposed to enable AV behaviour prediction systems to predict trajectories and intentions for pedestrians and vehicles. This will be achieved by using information from previous literature review papers, recent works, and results obtained using well-known datasets. • An AV behaviour prediction system must have a long (> 3s) prediction horizon. • It is a challenging task to model interaction between agents. • Datasets from onboard sensors should be used to predict road agents' behaviour. • Deep learning techniques have shown better results to predict behaviours. • Algorithms should be evaluated using the same datasets and evaluation metrics. [ABSTRACT FROM AUTHOR]