Monocular Connected-Vehicle Position Estimation on Sloping and Uneven Roads

Assessing the relative position of surrounding vehicles is a core requirement of autonomous vehicles to make decisions and plan driving trajectories. Many approaches based on monocular vision are designed for flat roads but contain unpredictable errors in some corner cases, such as sloping and uneven roads. In this article, the proposed method focuses on a relative-distance measurement adaptive to rough roads, using monocular vision and the features from connected vehicles. The proposed model takes the perspective-n-point approach as a basic framework. A connected-vehicle network can provide the fixed topology of feature points on the target vehicles for position estimation. The fixed topology replaces the requirement of cameras’ extrinsic parameters, which may change unpredictably on rough roads. The proposed approach is implemented on real vehicles driving on sloping and uneven roads. The results are compared with Mobileye, a widely used product for relative positioning. The experiments take the real-time kinematic GPS as ground truth and show that the proposed method achieves decimeter-level measurements and outperforms Mobileye on sloping and uneven roads. This article also shows great potential to improve environment perception using the connected-vehicle network in the future.