Pedestrian trajectory prediction improves using maps and awareness states
MamMA: A Mamba-Based Pedestrian Trajectory Prediction Algorithm Considering Occupancy Map and Pedestrian Awareness States
Computer Vision and Pattern RecognitionMachine Learning
Summary
Predicting where people will walk helps robots move safely around them. The authors developed MamMA, a method that uses maps from laser sensors and also considers whether pedestrians are aware of their surroundings, which affects how they move. They break down the map into parts to better understand obstacles and use a Mamba-based model to predict pedestrian paths. Tests on several common datasets show that MamMA predicts movements more accurately than prior methods.
What this means in practice
- •For mobile robot developers: More accurately predict pedestrian movements by integrating LiDAR occupancy maps and awareness states to improve robot navigation safety.
- •For smart city planners: Enhance pedestrian flow models by incorporating awareness and obstacle-based map features to better design shared human-robot spaces.
Authors
Juncen Long, Xiaofeng Jin, Gianluca Bardaro, Simone Mentasti, Matteo Matteucci
Abstract
Many pedestrian trajectory prediction algorithms have been proposed to improve the safety of navigation for mobile robots working in human-robot coexistence environments. Some pedestrian trajectory prediction algorithms extract information about obstacles near pedestrians from top-down view images to improve the accuracy of trajectory prediction. However, mobile robots typically create local occupancy maps using LiDAR, rather than top-down view images. Meanwhile, the vision sensors on board robots provide egocentric view images, which contain fine-grained behavioral information about the pedestrians near the robot. To better use the information collected by LiDAR and on-board vision sensors, we propose MamMA, a Mamba-based pedestrian trajectory prediction algorithm considering occupancy maps and pedestrian awareness states. MamMA divides the occupancy map by patches and extracts obstacle features from each patch to create map features. Pedestrian awareness states are divided and considered, as some studies show that awareness states affect the perception and speed of pedestrians. Furthermore, a Mamba-based model is proposed to predict the future trajectories of pedestrians based on different types of features. Experiments on the STCrowd, SiT, JRDB, ETH, and UCY datasets show that MamMA achieves better average displacement error and final displacement error than the state-of-the-art algorithms.