Learning a Curve Guardian for Motorcycles
This addresses safety for motorcyclists by improving curve warning systems, though it is incremental as it builds on existing technologies like computer vision and mapping.
The paper tackles the problem of motorcycle accidents in curves, which account for up to 17% of accidents, by developing a road curvature warning system that predicts intra-lane position and roll angle using CNNs, incorporates road incline in control, and uses map data for scalability, resulting in more accurate and safer curve trajectory predictions.
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.