Reinforcement Learning for Dynamic Optimization of Lane Change Intention Recognition for Transportation Networks

Computer Science

• Transportation Network 100%
• Reinforcement Learning 100%
• Input Processing 100%
• Trajectory Data 100%
• Bidirectional Long Short-Term Memory Network 66%
• Conditional Random Field 66%
• Prediction Accuracy 33%
• Interdependence 33%
• Baseline Model 33%
• Attention (Machine Learning) 33%
• Performance Loss 33%
• Temporal Data 33%
• Main Component 33%

Engineering

• Reinforcement Learning 100%
• Long Short-Term Memory 25%
• Experimental Finding 25%
• Interdependence 25%
• Main Component 25%
• Input Data 25%
• Detection Technique 25%
• Recognition Model 25%
• Lstm 25%

Psychology

• Attention Process 50%
• Memory Network 50%