A Hybrid Control Architecture For Autonomous Driving In Urban Environment

Autonomous driving in an urban environment is one of the most actively studied topics. To date, many studies on autonomous driving can be classified into two main approaches: 1. local perception-based approach 2. global path tracking-based approach. However, each approach has its own limitations for fully autonomous driving. In the case of perception-based approach, it is impossible to autonomously drive to the global destination because it only runs locally within the sensor range. On the other hand, the path tracking-based approach relies heavily on accurate navigation. For accurate navigation, there are many studies using expensive equipment or extremely precise and detailed maps, but they have not been resolved yet, and they are also not practical. In this paper, we address the problem of autonomous driving in an urban environment through the proposed hybrid control architecture. Proposed control architecture is designed to be complementary of local perception-based and global path tracking-based approaches. Especially, end-to-end deep learning based autonomous driving, which mimics human driving, is applied as a local perception-based approach. In addition, path tracking-based autonomous driving is performed in an environment where directional information to the destination is required, such as intersections. At the same time, our hybrid control architecture effectively compensates for the navigation error using ICP matching between the perception-based driven trajectory and the global path to the destination without any highly detailed prior map or expensive equipment. The performance of the proposed architecture on a full-scale autonomous vehicle is verified through experiments in the urban environment.