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Autonomous vehicles require motion forecasting of their surroundingmultiagents (pedestrians and vehicles) to make optimal decisions fornavigation. The existing methods focus on techniques to utilize the positionsand velocities of these agents and fail to capture semantic information fromthe scene. Moreover, to mitigate the increase in computational complexityassociated with the number of agents in the scene, some works leverageEuclidean distance to prune far-away agents. However, distance-based metricalone is insufficient to select relevant agents and accurately perform theirpredictions. To resolve these issues, we propose the Semantics-awareInteractive Multiagent Motion Forecasting (SIMMF) method to capture semanticsalong with spatial information and optimally select relevant agents for motionprediction. Specifically, we achieve this by implementing a semantic-awareselection of relevant agents from the scene and passing them through anattention mechanism to extract global encodings. These encodings along withagents' local information, are passed through an encoder to obtaintime-dependent latent variables for a motion policy predicting the futuretrajectories. Our results show that the proposed approach outperformsstate-of-the-art baselines and provides more accurate and scene-consistentpredictions.

SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving