Simulation framework to train intelligent agents towards an assisted driving power wheelchair for people with disability

Several million people with disabilities exploit power wheelchairs for outdoor mobility on both sidewalks and cycling paths. Especially those with upper limb motor impairments have difficulty reacting quickly to obstacles along the way, creating dangerous situations, such as wheelchair crash or rollover. A possible solution could be to equip the power wheelchair with a neural network-based assisted driving system, able to detect, avoid or warn the users of obstacles. Therefore, a virtual environment is required to simulate the system and then test different neural network architectures before mounting the best performing one directly on board. In this work, we present a simulation framework to train multiple artificial intelligent agents in parallel, by means of reinforcement learning algorithms. The agent shall follow the user's will and identify obstacles along the path, taking the control of the power wheelchair when the user is making a dangerous driving choice. The developed framework, adapted from an existing autonomous driving simulator, has been used to train and test multiple intelligent agents simultaneously, thanks to a customised synchronisation and memory management mechanism, reducing the overall training time. Preliminary results highlight the suitability of the adapted framework for multiple agent development in the assisted driving scenario.