Extended Abstract: Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

Abstract

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior, which could be improved by accurate and reliable prediction models enabling more efficient trajectory planning. However, the evaluation of such models is commonly over-simplistic, ignoring the asymmetric importance of prediction errors and the heterogeneity of the datasets used for testing. We examine the potential of recasting interactions between vehicles as gap acceptance scenarios and evaluating models in this structured environment. To that end, we develop a framework aiming to facilitate the evaluation of any model, by any metric, and in any scenario. We then apply this framework to state-of-the-art prediction models, which all show themselves to be unreliable in the most safety-critical situations.