Behavior Identification and Prediction for a Probabilistic Risk Framework
This addresses safety challenges for autonomous vehicles in real-world traffic, though it appears incremental as it builds on existing MMAE methods.
The paper tackles the problem of autonomous vehicle motion planning in complex traffic environments by developing a novel behavior identification and prediction system using Multi Model Adaptive Estimation (MMAE) to analyze risks from predicted trajectories, with preliminary simulation results included.
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.