Trajectory-Prediction with Vision: A Survey
It addresses the need for safe route planning in autonomous vehicles by summarizing existing research, but it is incremental as it does not introduce new methods or results.
This paper provides a survey of trajectory-prediction methods for autonomous vehicles, categorizing algorithms and discussing background knowledge to help researchers understand trends in the field.
To plan a safe and efficient route, an autonomous vehicle should anticipate future trajectories of other agents around it. Trajectory prediction is an extremely challenging task which recently gained a lot of attention in the autonomous vehicle research community. Trajectory-prediction forecasts future state of all the dynamic agents in the scene given their current and past states. A good prediction model can prevent collisions on the road, and hence the ultimate goal for autonomous vehicles: Collision rate: collisions per Million miles. The objective of this paper is to provide an overview of the field trajectory-prediction. We categorize the relevant algorithms into different classes so that researchers can follow through the trends in the trajectory-prediction research field. Moreover we also touch upon the background knowledge required to formulate a trajectory-prediction problem.