Long-term vehicle motion prediction

Abstract

Future driver assistance systems will have to cope with complex traffic situations, especially in the road crossing scenario. To detect potentially hazardous situations as early as possible, it is therefore desirable to know the position and motion of the ego-vehicle and vehicles around it for several seconds in advance. For this purpose, we propose in this study a long-term prediction approach based on a combined trajectory classification and particle filter framework. As a measure for the similarity between trajectories, we introduce the quaternion-based rotationally invariant longest common subsequence (QRLCS) metric. The trajectories are classified by a radial basis function (RBF) classifier with an architecture that is able to process trajectories of arbitrary non-uniform length. The particle filter framework simultaneously tracks and assesses a large number of motion hypotheses (~10 ² ), where the class-specific probabilities estimated by the RBF classifier are used as a-priori probabilities for the hypotheses of the particle filter. The hypotheses are clustered with a mean-shift technique and are assigned a likelihood value. Motion prediction is performed based on the cluster centre with the highest likelihood. While traditional motion prediction based on curve radius and acceleration is inaccurate especially during turning manoeuvres, we show that our approach achieves a reasonable motion prediction even for long prediction intervals of 3 s for these complex motion patterns.