Prediction Based Decision Making for Autonomous Highway Driving
This addresses safer autonomous driving for highway scenarios, though it appears incremental by adding prediction to an existing DRL approach.
The paper tackles autonomous highway driving decision-making by proposing a Prediction-based Deep Reinforcement Learning (PDRL) model that incorporates surrounding vehicle intentions, trained on real traffic data and tested in simulation. The results show it improves performance over a baseline DRL model by decreasing collision numbers for safer driving.
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.