FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings

• URL: http://arxiv.org/abs/2304.06121v1
• Date: Wed, 12 Apr 2023 19:05:56 GMT
• Title: FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings
• Authors: Abduallah Mohamed, Jundi Liu, Linda Ng Boyle, Christian Claudel
• Abstract summary: We introduce a new dataset, the FollowMe dataset, which offers a motion and behavior prediction problem. We show the design benefits of FollowMe-STGCNN in capturing the interactions that lie within the dataset.
• Score: 2.294014185517203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An ego vehicle following a virtual lead vehicle planned route is an essential component when autonomous and non-autonomous vehicles interact. Yet, there is a question about the driver's ability to follow the planned lead vehicle route. Thus, predicting the trajectory of the ego vehicle route given a lead vehicle route is of interest. We introduce a new dataset, the FollowMe dataset, which offers a motion and behavior prediction problem by answering the latter question of the driver's ability to follow a lead vehicle. We also introduce a deep spatio-temporal graph model FollowMe-STGCNN as a baseline for the dataset. In our experiments and analysis, we show the design benefits of FollowMe-STGCNN in capturing the interactions that lie within the dataset. We contrast the performance of FollowMe-STGCNN with prior motion prediction models showing the need to have a different design mechanism to address the lead vehicle following settings.

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