  This paper presents a Predictive Maneuver Planning with Deep Reinforcement
Learning (PMP-DRL) model for maneuver planning. Traditional rule-based maneuver
planning approaches often have to improve their abilities to handle the
variabilities of real-world driving scenarios. By learning from its experience,
a Reinforcement Learning (RL)-based driving agent can adapt to changing driving
conditions and improve its performance over time. Our proposed approach
combines a predictive model and an RL agent to plan for comfortable and safe
maneuvers. The predictive model is trained using historical driving data to
predict the future positions of other surrounding vehicles. The surrounding
vehicles' past and predicted future positions are embedded in context-aware
grid maps. At the same time, the RL agent learns to make maneuvers based on
this spatio-temporal context information. Performance evaluation of PMP-DRL has
been carried out using simulated environments generated from publicly available
NGSIM US101 and I80 datasets. The training sequence shows the continuous
improvement in the driving experiences. It shows that proposed PMP-DRL can
learn the trade-off between safety and comfortability. The decisions generated
by the recent imitation learning-based model are compared with the proposed
PMP-DRL for unseen scenarios. The results clearly show that PMP-DRL can handle
complex real-world scenarios and make better comfortable and safe maneuver
decisions than rule-based and imitative models.
