  We consider the problem of maximizing distance to road agents for a
self-driving car. To this extent, we employ a Model Predictive Control (MPC)
approach for the steering tracking control of an Autonomous Vehicle (AV).
Specifically, we first present a traditional MPC controller, which is then
extended to encode the clearance maximization goal by manipulating its cost
function and constraints. We provide insights on the additional information
needed to achieve such goal, and how this modifies the structure of the
original controller. Furthermore, a connection between commonly used safety
metrics and clearance to road users is established. We implement the MPC
controller using two off-the-shelf numerical solvers, assessing its
computational feasibility. Finally, we show experimental results of the
proposed approach on public roads in Boston and in Singapore.
