Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation

Abstract

Motion control algorithms in the presence of pedestrians are critical for the development of safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on manually designed decision-making policies which neglect the mutual interactions between AVs and pedestrians. On the other hand, recent advances in Deep Reinforcement Learning allow for the automatic learning of policies without manual designs. To tackle the problem of decision-making in the presence of pedestrians, the authors introduce a framework based on Social Value Orientation and Deep Reinforcement Learning (DRL) that is capable of generating decision-making policies with different driving styles. The policy is trained using state-of-the-art DRL algorithms in a simulated environment. A novel computationally-efficient pedestrian model that is suitable for DRL training is also introduced. We perform experiments to validate our framework and we conduct a comparative analysis of the policies obtained with two different model-free Deep Reinforcement Learning Algorithms. Simulations results show how the developed model exhibits natural driving behaviours, such as short-stopping, to facilitate the pedestrian's crossing.

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Cited by in corpus (7)

• Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction
• Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings
• IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning
• Decision Making in Urban Traffic: A Game Theoretic Approach for Autonomous Vehicles Adhering to Traffic Rules
• Can we enhance prosocial behavior? Using post-ride feedback to improve micromobility interactions
• How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games
• Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework