Hybrid Local Route Generation Combining Perception and a Precise Map for Autonomous Cars

Abstract

Autonomous cars are currently widely studied in the automotive and robotics industries because autonomous driving can satisfy the needs of human drivers regarding safety, efficiency, and comfortable driving. Behavior and motion planning for an autonomous car is one of the main requirements of autonomous driving. For autonomous cars, a local route along with its road geometry and attributes of the area is required. There are two ways to obtain the local route: perception-based local route (PBLR) and precise map-based local route (MBLR) methods. First, this paper analyzes the characteristic of both the PBLR and MBLR inference methods. Then based on this analysis, this paper proposes a hybrid local route generation algorithm that chooses the best local route between the PBLR and MBLR options, according to the perceived performance and the precise map availability. To effectively create an expensive precise map, a mapping region classification algorithm is presented to selectively choose the mapping area, where the precise map must be constructed for the MBLR inference. The hybrid local route generation algorithm with the mapping region classification allows the area used for autonomous driving to be extended while reducing the cost due to the precise map. The advantages of the proposed algorithm were verified with experiments in real traffic conditions.