Based on the results, it can be concluded that there is no disadvantage to using a safety layer when doing reinforcement learning since execution time is very similar. Moreover, the dramatically improved safety of the vehicle is helpful for the robot’s training as fewer actions with lower or even negative rewards will be executed. Because of this, reinforcement learning agents with safety layers learn faster and reduce the number of unsafe actions that are being executed.

Unfortunately, manual observation and intervention by the user were still necessary, however, the frequency was clearly reduced which further improved learning as the robots in testing did not know if an outside intervention was done which could result in an action being rewarded incorrectly.

It was also concluded that this project did not reach perfect safety with the implementation. Therefore a fully autonomous reinforcement learning training without any human intervention has not yet been achieved. A lot of improvement factors have been found that can further improve the safety and recovery rate. Additionally, some major problems which are not direct results of the reinforcement learning or safety layer have been identified.

These problems could be attempted to be fixed in different ways like improving the open source implementations of lane filter nodes or adding more sensors or cameras to the robot in order to extend the input data to the agent. Another area that was untouched during the research of this project was other vehicles inside the current lane. The safety layer could potentially be extended to also include safety features that should keep the robot safe from hitting other vehicles.

Read the full report here.