Global Trajectory Optimization on Multilane Roads (original) (raw)

2015

Abstract

This paper names several risks with iterative optimization of vehicle trajectories modeled through the calculus of variations, as used recently in several approaches to automated driving, and demonstrates how these can be addressed by global optimization, while retaining the original, variational optimization goals. A method for transforming variational methods into Hidden Markov Models is applied to multilane scenarios, such as highways or larger city streets. A particular side-effect of this method is the capability of providing a fail-safe emergency trajectory at almost no further computational effort. The proposed global optimization is demonstrated on several real-world situations and compared to a ground truth of human maneuvers.

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