Markov Model-based Static Obstacle Map Estimation for Perception of Automated Driving

자율주행 인지를 위한 마코브 모델 기반의 정지 장애물 추정 연구

Abstract

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.

Fig. 1 Sensor configuration

Fig. 2 Detection area of 6 LiDAR (Bird eye view)

Fig. 3 Detection area of 6 LiDAR (Side view)

Fig. 4 Process of probability density construction

Fig. 5 Diagram of Hidden Markov model for suggested method

Fig. 6 SNU Si-heung campus : straight course at the top, Intersection course at the bottom

Fig. 7 Result of scenario 1

Fig. 8 Result of scenario 2

Fig. 9 Result of scenario 3