Modeling Crossing Behaviors of E-Bikes at Intersection With Deep Maximum Entropy Inverse Reinforcement Learning Using Drone-Based Video Data
Author:
Affiliation:
1. College of Transportation Engineering, Chang’an University, Xi’an, China
2. School of Transportation, Southeast University, Nanjing, China
3. School of Economics and Management, Chang’an University, Xi’an, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shaanxi Province
China Scholarship Council
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Science Applications,Mechanical Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/6979/10139322/10061370.pdf?arnumber=10061370
Reference57 articles.
1. Modeling Pedestrian Temporal Violations at Signalized Crosswalks: A Random Intercept Parametric Survival Model
2. Future Trajectory Prediction via RNN and Maximum Margin Inverse Reinforcement Learning
3. Social force model for pedestrian dynamics
4. Analyzing the Suitability of Cost Functions for Explaining and Imitating Human Driving Behavior based on Inverse Reinforcement Learning
5. Modeling pedestrian behavior in pedestrian-vehicle near misses: A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach
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