  In this article we design a physically-inspired model-based assist-as-needed
semi-autonomous control (ASC) algorithm to address the problem of safely
driving a vehicle (a power wheelchair) in an environment with static obstacles.
Once implemented online, the proposed algorithm requires limited computing
power and relies on pre-computed (offline) maps (look-up tables). These are
readily available by implementing policy iteration that minimizes the expected
time to termination (safely stopping near an obstacle), by taking into account:
(i) the vehicle dynamics; (ii) the drivers' intention modeled as three separate
stochastic processes. We call them the expert driver, the naughty child and the
blind driver models. A study with healthy participants confirmed that ASC
outperforms a baseline rule-based control (a statistically significant result).
