  In automated driving, predicting and accommodating the uncertain future
motion of other traffic participants is challenging, especially in unstructured
environments in which the high-level intention of traffic participants is
difficult to predict. Several possible uncertain future behaviors of traffic
participants must be considered, resulting in multi-modal uncertainty. We
propose a novel combination of Belief Function Theory and Stochastic Model
Predictive Control for trajectory planning of the autonomous vehicle in
presence of significant uncertainty about the intention estimation of traffic
participants. A misjudgment of the intention of traffic participants may result
in dangerous situations. At the same time, excessive conservatism must be
avoided. Therefore, the measure of reliability of the estimation provided by
Belief Function Theory is used in the design of collision-avoidance safety
constraints, in particular to increase safety when the intention of traffic
participants is not clear. We discuss two methods to leverage on Belief
Function Theory: we introduce a novel belief-to-probability transformation
designed not to underestimate unlikely events if the information is uncertain,
and a constraint tightening mechanism using the reliability of the estimation.
We evaluate our proposal through simulations comparing to state-of-the-art
approaches.
