Reinforcement Learning for Switching of Semiautomated Vehicles Under Uncomfortable Driving Situations

Over the last two decades, autonomous driving has progressed from science fiction to a real possibility and rapidly developing. However, autonomous driving technology has significant weaknesses and is not safe in unexpected conditions. As a result, automobile manufacturers insist that the driver remains in the driver's seat even while the...

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...

Reinforcement Learning approach for decision-making in driver control shifting for semi-autonomous driving

Semi-autonomous driving innovations aim to bridge the gap to fully autonomous driving by co-operating with human drivers to lead to optimal choices on who should drive in different scenarios by offering different automation levels. However, in the present day, known semi-autonomous driving solutions do not generalise to every complex case of...

Optimizing driving entity switching of semi-automated vehicles under automation degradation

Transitioning to use automated vehicles is a gradual process. Until full automation capabilities are developed there is a need to mediate which driving entity - human or autonomous driving system (ADS) - should be in control depending on the circumstances. This research aims at investigating the switching between manual and automated driving in...