車線方位を考慮した補助損失による自動運転の軌道予測
Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss
車線の進行方向に反する軌道を罰する微分可能な新指標を補助損失として導入し、MTPやMultiPathモデルを拡張してnuScenesベンチマークで車線追従性の高い予測を実現した。
著者: Ross Greer, Nachiket Deo, Mohan Trivedi
分類: cs.CV, cs.RO
原文アブストラクト
Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes. Bird's-eye-view roadmap information provides valuable information for making trajectory predictions, and while state-of-the-art models extract this information via image convolution, auxiliary loss functions can augment patterns inferred from deep learning by further encoding common knowledge of social and legal driving behaviors. Since human driving behavior is inherently multimodal, models which allow for multimodal output tend to outperform single-prediction models on standard metrics. We propose a loss function which enhances such models by enforcing expected driving rules on all predicted modes. Our contribution to trajectory prediction is twofold; we propose a new metric which addresses failure cases of the off-road rate metric by penalizing trajectories that oppose the ascribed heading (flow direction) of a driving lane, and we show this metric to be differentiable and therefore suitable as an auxiliary loss function. We then use this auxiliary loss to extend the the standard multiple trajectory prediction (MTP) and MultiPath models, achieving improved results on the nuScenes prediction benchmark by predicting trajectories which better conform to the lane-following rules of the road.
