車線変更・合流を考慮した軌跡予測のためのマヌーバ認識プーリング
Maneuver-Aware Pooling for Vehicle Trajectory Prediction
周辺車両の極座標表現と向き・動径速度を用いたプーリングで、車線変更や合流の意図を暗黙的に考慮し、生成型エンコーダ・デコーダで高速道路の車両軌跡予測精度を向上させた研究。
著者: Mohamed Hasan, Albert Solernou, Evangelos Paschalidis, He Wang, Gustav Markkula, Richard Romano
分類: cs.CV, cs.RO
原文アブストラクト
Autonomous vehicles should be able to predict the future states of its environment and respond appropriately. Specifically, predicting the behavior of surrounding human drivers is vital for such platforms to share the same road with humans. Behavior of each of the surrounding vehicles is governed by the motion of its neighbor vehicles. This paper focuses on predicting the behavior of the surrounding vehicles of an autonomous vehicle on highways. We are motivated by improving the prediction accuracy when a surrounding vehicle performs lane change and highway merging maneuvers. We propose a novel pooling strategy to capture the inter-dependencies between the neighbor vehicles. Depending solely on Euclidean trajectory representation, the existing pooling strategies do not model the context information of the maneuvers intended by a surrounding vehicle. In contrast, our pooling mechanism employs polar trajectory representation, vehicles orientation and radial velocity. This results in an implicitly maneuver-aware pooling operation. We incorporated the proposed pooling mechanism into a generative encoder-decoder model, and evaluated our method on the public NGSIM dataset. The results of maneuver-based trajectory predictions demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches. Our "Pooling Toolbox" code is available at https://github.com/m-hasan-n/pooling.
