TY - GEN
T1 - Pedestrian Trajectory Prediction using BiLSTM with Spatial-Temporal Attention and Sparse Motion Fields
AU - Khel, Muhammad Haris Kaka
AU - Greaney, Paul
AU - McAfee, Marion
AU - Moffett, Sandra
AU - Meehan, Kevin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Autonomous cars and mobile robots need to be able to anticipate and interpret pedestrian intents in order to manoeuvre safely in a congested area. However, because pedestrian movement is unpredictable, it is necessary to take into account a variety of elements when modelling their potential trajectories, including their prior movements, interactions with other pedestrians, and restrictions imposed by static objects in the environment. Many current trajectory prediction techniques ignore the possible influence of static impediments on pedestrian movement, and instead have concentrated only on how pedestrians interact with one another in a given scenario. Furthermore, many of these techniques need additional data such as semantic maps, which reduces scalability and may not always be available. Our proposed model, named scene and social interaction using biLSTM, Spatial-Temporal Attention and Sparse Motion Field (SS-BLSTAS), uses simply the trajectory of pedestrians as input and is accurate at predicting future movement, by considering both the presence of obstacles and social interactions. The proposed model is compared to other state-of-the-art models by using standard evaluation metrics on the ETH and UCY pedestrian datasets. The experimental results show that the proposed model performs better than most of the benchmarked approaches.
AB - Autonomous cars and mobile robots need to be able to anticipate and interpret pedestrian intents in order to manoeuvre safely in a congested area. However, because pedestrian movement is unpredictable, it is necessary to take into account a variety of elements when modelling their potential trajectories, including their prior movements, interactions with other pedestrians, and restrictions imposed by static objects in the environment. Many current trajectory prediction techniques ignore the possible influence of static impediments on pedestrian movement, and instead have concentrated only on how pedestrians interact with one another in a given scenario. Furthermore, many of these techniques need additional data such as semantic maps, which reduces scalability and may not always be available. Our proposed model, named scene and social interaction using biLSTM, Spatial-Temporal Attention and Sparse Motion Field (SS-BLSTAS), uses simply the trajectory of pedestrians as input and is accurate at predicting future movement, by considering both the presence of obstacles and social interactions. The proposed model is compared to other state-of-the-art models by using standard evaluation metrics on the ETH and UCY pedestrian datasets. The experimental results show that the proposed model performs better than most of the benchmarked approaches.
KW - Attention
KW - BiLSTM
KW - Interactions
KW - Obstacles
KW - Path Prediction
UR - http://www.scopus.com/inward/record.url?scp=85165950291&partnerID=8YFLogxK
U2 - 10.1109/ISSC59246.2023.10162063
DO - 10.1109/ISSC59246.2023.10162063
M3 - Conference contribution
AN - SCOPUS:85165950291
T3 - 2023 34th Irish Signals and Systems Conference, ISSC 2023
BT - 2023 34th Irish Signals and Systems Conference, ISSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th Irish Signals and Systems Conference, ISSC 2023
Y2 - 13 June 2023 through 14 June 2023
ER -