Joint driver intention classification and tracking of vehicles
Paper i proceeding, 2006
In this paper we present and validate a new modelling framework
for joint driver intention classification and tracking of
vehicles; a framework derived for automotive active safety
systems. Such systems require reliable predictions of the traffic
situation to act in time when a dangerous situation occur.
Our proposal has two main benefits. First, it incorporates
the intention of the driver into the vehicle motion model and
thereby improves the prediction capability. The result is a
multiple motion model where each model corresponds to a
specific driver intent. Second, the connection between different
driver plans and corresponding motion model enables a
formal classification of the most likely driver intention. To
validate our concept, we apply the motion model on real data
using a particle filter implementation. Initial studies indicate
promising performance.