Reinforcement Learning for Switching Control of Semi-automated Vehicles with Driver Fatigue

Automated driving is a rapidly growing technology nowadays. Semi-automated driving is a subpart of automated driving which has multiple driving modes where both driver and automated module can take control. But full safety and comfort guarantees cannot still be given to the drivers. In this project, research has been done to ensure driver safety and comfort for the driver on the decision logic module of a semi-automated vehicle. Research focuses on just one specific use-case which is driver fatigue. The main goal is to ensure driver safety and comfort in the case where the user is sleepy during manual driving. Markov Decision Process is used to create a model to successfully represent this case. Evaluation has been done using already existing Reinforcement Learning algorithms and comparing their performances with the decision-tree based baseline policy. In conclusion, a successful Markov Decision Process model is created. While evaluating, some models performed better than expected and some are worse. In the end, a successful model is proposed, it can still be developed further to provide more safety and comfort for the driver