AI/ML

Behavior Identification and Prediction for a Probabilistic Risk Framework. (arXiv:1905.08332v1 [cs.RO])

Operation in a real world traffic requires autonomous vehicles to be able to
plan their motion in complex environments (multiple moving participants).
Planning through such environment requires the right search space to be
provided for the trajectory or maneuver planners so that the safest motion for
the ego vehicle can be identified. Given the current states of the environment
and its participants, analyzing the risks based on the predicted trajectories
of all the traffic participants provides the necessary search space for the
planning of motion. This paper provides a fresh taxonomy of safety / risks that
an autonomous vehicle should be able to handle while navigating through
traffic. It provides a reference system architecture that needs to be
implemented as well as describes a novel way of identifying and predicting the
behaviors of the traffic participants using classic Multi Model Adaptive
Estimation (MMAE). Preliminary simulation results of the implemented model are
included.

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