Navigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.

Authors

• David Isele
• Reza Rahimi
• Akansel Cosgun
• Kaushik Subramanian
• Kikuo Fujimura

Keywords

• Autonomous vehicles
• Automobiles
• Machine learning
• Safety
• Navigation
• Learning (artificial intelligence)
• Deep Learning
• Deep Reinforcement Learning
• Task Completion Time
• Effective Deep Learning
• Time Step
• Real-valued
• Sequence Of Actions
• Traffic Congestion
• State Representation
• Exploratory Activity
• Realistic Representation
• Action Representation
• Self-driving
• Markov Decision Process
• First Set Of Experiments
• Challenging Scenarios
• Departure Time
• Human Drivers
• Optimal Value Function
• Deep Q-network
• Imitation Learning
• Action-value Function
• Offline Learning
• Heading Angle
• Time Scenarios
• Exploratory Behavior
• Value Function