Does Unpredictability Influence Driving Behavior?

In this paper we investigate the effect of the unpredictability of surrounding cars on an ego-car performing a driving maneuver. We use Maximum Entropy Inverse reinforcement Learning to model reward functions for an ego-car conducting a lane change in a highway setting. We define a new feature based on the unpredictability of surrounding cars and use it in the reward function. We learn two reward functions from human data: a baseline and one that incorporates our defined unpredictability feature, then compare their performance with a quantitative and qualitative evaluation. Our evaluation demonstrates that incorporating the unpredictability feature leads to a better fit of human-generated test data. These results encourage further investigation of the effect of unpredictability on driving behavior.

Authors

• Sepehr Samavi
• Florian Shkurti
• Angela P. Schoellig

Keywords

• Road transportation
• Measurement
• Reinforcement learning
• Predictive models
• Entropy
• Trajectory
• Behavioral sciences
• Human Data
• Reward Function
• Lane Change
• Inverse Reinforcement Learning
• Prediction Model
• Model Performance
• Training Set
• Deep Neural Network
• Human Behavior
• Test Dataset
• Long Short-term Memory
• Parametrized
• Autonomous Vehicles
• Variational Autoencoder
• Reinforcement Learning Approach
• Human Drivers
• Unpredictable Behavior
• Trajectory Generation
• Linear Combination Of Features
• Human Ones
• Human Trajectory
• Trajectory Dataset
• Lane Change Maneuver
• Green Vehicle
• Continuous Action Space
• Test Scenarios
• Performance Metrics
• Continuous State Space
• Traffic Congestion