Researchers have developed MamMA, a novel algorithm for predicting pedestrian trajectories, designed to enhance the safety of mobile robots operating in environments with human presence. This Mamba-based model integrates occupancy map data, typically generated by LiDAR, with fine-grained behavioral information from on-board vision sensors. By dividing occupancy maps into patches and considering pedestrian awareness states, MamMA aims to improve prediction accuracy. Experiments on several benchmark datasets indicate that MamMA outperforms existing state-of-the-art algorithms in terms of average and final displacement error. AI
IMPACT This research could lead to safer navigation for autonomous robots in human-populated areas by improving their ability to predict pedestrian movements.
RANK_REASON This is a research paper detailing a new algorithm and its experimental results on benchmark datasets. [lever_c_demoted from research: ic=1 ai=1.0]
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