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

• URL: http://arxiv.org/abs/2306.14941v2
• Date: Thu, 12 Oct 2023 22:49:49 GMT
• Title: SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving
• Authors: Vidyaa Krishnan Nivash, Ahmed H. Qureshi
• Abstract summary: We propose a semantic-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture semantics along with spatial information. Specifically, we achieve this by implementing a semantic-aware selection of relevant agents from the scene and passing them through an attention mechanism. Our results show that the proposed approach outperforms state-of-the-art baselines and provides more accurate and scene-consistent predictions.
• Score: 2.7195102129095003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians and vehicles) to make optimal decisions for navigation. The existing methods focus on techniques to utilize the positions and velocities of these agents and fail to capture semantic information from the scene. Moreover, to mitigate the increase in computational complexity associated with the number of agents in the scene, some works leverage Euclidean distance to prune far-away agents. However, distance-based metric alone is insufficient to select relevant agents and accurately perform their predictions. To resolve these issues, we propose the Semantics-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture semantics along with spatial information and optimally select relevant agents for motion prediction. Specifically, we achieve this by implementing a semantic-aware selection of relevant agents from the scene and passing them through an attention mechanism to extract global encodings. These encodings along with agents' local information, are passed through an encoder to obtain time-dependent latent variables for a motion policy predicting the future trajectories. Our results show that the proposed approach outperforms state-of-the-art baselines and provides more accurate and scene-consistent predictions.

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