MULTI-AGENT TRAJECTORY PREDICTION FOR AUTONOMOUS VEHICLES

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.