クラス不確実性を考慮した異種エージェント軌道予測
Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty
エージェントのクラス確率分布を明示的に取り込み、不確実性下での軌道予測性能を向上させる手法HAICUと、知覚不確実性を検証する実世界自動運転データセットPUPを提案した。
著者: Boris Ivanovic, Kuan-Hui Lee, Pavel Tokmakov, Blake Wulfe, Rowan McAllister, Adrien Gaidon, Marco Pavone
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Reasoning about the future behavior of other agents is critical to safe robot navigation. The multiplicity of plausible futures is further amplified by the uncertainty inherent to agent state estimation from data, including positions, velocities, and semantic class. Forecasting methods, however, typically neglect class uncertainty, conditioning instead only on the agent's most likely class, even though perception models often return full class distributions. To exploit this information, we present HAICU, a method for heterogeneous-agent trajectory forecasting that explicitly incorporates agents' class probabilities. We additionally present PUP, a new challenging real-world autonomous driving dataset, to investigate the impact of Perceptual Uncertainty in Prediction. It contains challenging crowded scenes with unfiltered agent class probabilities that reflect the long-tail of current state-of-the-art perception systems. We demonstrate that incorporating class probabilities in trajectory forecasting significantly improves performance in the face of uncertainty, and enables new forecasting capabilities such as counterfactual predictions.
