Prediction Based Decision Making for Autonomous Highway Driving

2022-09-05 · Mustafa Yıldırım, Sajjad Mozaffari, Luc McCutcheon, Mehrdad Dianati, Alireza Tamaddoni-Nezhad Saber Fallah

Autonomous driving decision-making is a challenging task due to the inherent complexity and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake at any time to pass a slow vehicle or to help traffic flow. Anticipating the intention of surrounding vehicles, estimating their future states and integrating them into the decision-making process of an automated vehicle can enhance the reliability of autonomous driving in complex driving scenarios. This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving. The model is trained using real traffic data and tested in various traffic conditions through a simulation platform. The results show that the proposed PDRL model improves the decision-making performance compared to a Deep Reinforcement Learning (DRL) model by decreasing collision numbers, resulting in safer driving.

📄 PDF Abstract BibTeX arXiv:2209.02106

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Autonomous Driving Decision MakingDeep Reinforcement LearningPrediction reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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