Dependent Hidden Markov Model for pedestrian intention prediction: considering Multivariate Interaction Force
Author:
Affiliation:
1. School of Automation, Nanjing University of Science and Technology, Nanjing, People’s Republic of China
Funder
National Natural Science Foundation of China
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/23249935.2024.2373921
Reference28 articles.
1. Multi-scale pedestrian intent prediction using 3D joint information as spatio-temporal representation
2. Alahi Alexandre Kratarth Goel Vignesh Ramanathan Alexandre Robicquet Fei-Fei Li Silvio Savarese and IEEE. 2016. Social LSTM: Human Trajectory Prediction in Crowded Spaces.” Paper presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Seattle WA 2016 Jun 27–30.
3. Chaabane Mohamed Ameni Trabelsi Nathaniel Blanchard Ross Beveridge and IEEE Comp Soc. 2020. “Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction.” Paper presented at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Snowmass CO 2020 Mar 01–05.
4. Lane-based Distance-Velocity model for evaluating pedestrian-vehicle interaction at non-signalized locations
5. Predicting Intentions of Pedestrians from 2D Skeletal Pose Sequences with a Representation-Focused Multi-Branch Deep Learning Network
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