ギャップ受容シナリオにおける人間行動予測モデルのベンチマーク
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
車両間のインタラクションをギャップ受容シナリオとして再構成し、任意のモデル・指標・シナリオで評価できるフレームワークを構築した。最先端の予測モデルを評価した結果、最も安全上重要な状況で信頼性が低いことが示された。
著者: Julian Frederik Schumann, Jens Kober, Arkady Zgonnikov
分類: cs.RO, cs.LG
原文アブストラクト
Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is commonly oversimplistic, 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.
