カーブを走行する二輪車のためのカーブガーディアンの学習
Learning a Curve Guardian for Motorcycles
CNNによる車線内位置とロール角の推定、地図データを活用した道路形状取得、傾斜を考慮した制御モデルを組み合わせ、カーブ走行時の二輪車事故を防ぐ警告システムを提案した。
著者: Simon Hecker, Alexander Liniger, Henrik Maurenbrecher, Dengxin Dai, Luc Van Gool
分類: cs.CV, cs.RO, cs.SY, eess.SY
原文アブストラクト
Up to 17% of all motorcycle accidents occur when the rider is maneuvering through a curve and the main cause of curve accidents can be attributed to inappropriate speed and wrong intra-lane position of the motorcycle. Existing curve warning systems lack crucial state estimation components and do not scale well. We propose a new type of road curvature warning system for motorcycles, combining the latest advances in computer vision, optimal control and mapping technologies to alleviate these shortcomings. Our contributes are fourfold: 1) we predict the motorcycle's intra-lane position using a convolutional neural network (CNN), 2) we predict the motorcycle roll angle using a CNN, 3) we use an upgraded controller model that incorporates road incline for a more realistic model and prediction, 4) we design a scale-able system by utilizing HERE Technologies map database to obtain the accurate road geometry of the future path. In addition, we present two datasets that are used for training and evaluating of our system respectively, both datasets will be made publicly available. We test our system on a diverse set of real world scenarios and present a detailed case-study. We show that our system is able to predict more accurate and safer curve trajectories, and consequently warn and improve the safety for motorcyclists.
