RADHAR will develop a driving assistance system involving environment perception, driver perception and modelling, and robot decision making. RADHAR proposes a framework to seamlessly fuse the inherently uncertain information from both environment perception and the driver's steering signals by estimating the trajectory the robot should execute, and to adopt this fused information for safe navigation with a level of autonomy adjusted to the user's capabilities and desires. This requires lifelong, unsupervised but safe learning by the robot. As a consequence, a continuous interaction between two learning systems (the robot and the user) will emerge, hence Robotic ADaptation to Humans Adapting to Robots (RADHAR). The framework will be demonstrated on a robotic wheelchair platform that navigates in an everyday environment with everyday objects. RADHAR targets as main scientific outcomes: online 3D perception combining laser scanners and vision with traversability analysis of the terrain; novel paradigm for fusing environment and user perception and for safe robot navigation.