Improved Environment Recognition Algorithms for Autonomous Vehicle Control

자율주행 제어를 위한 향상된 주변환경 인식 알고리즘

Abstract

This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

Fig. 1 The experimental platform for the autonomous system

Fig. 2 Temperature change before/after control PC tuning

Fig. 3 Structure of the autonomous vehicles system

Fig. 4 Software architecture of Hye Ahn program

Fig. 8 SVM decision tree

Fig. 9 Grid for SVM feature extraction

Fig. 10 Accuracy of MVP-CS with conventional method by Distance

Fig. 11 Accuracy of MVP-CS and MVP-YOLO by distance

Fig. 12 ROI for obstacle detection

Fig. 13 Setting the length of the ROI

Fig. 14 Setting the offset value

Fig. 15 Setting the candidate of danger point

Fig. 16 Operation principle of obstacle avoid algorithm

Fig. 17 The areas where avoidance algorithm cannot work

Fig. 5 (a) Before and (b) after applying HIX algorithm

Fig. 6 (a) Input Image, (b) ELIX rescan, (c) ILT rescan 1 and (d) ILT rescan 2

Fig. 7 (a) Before and (b) after applying NPC algorithm

Table 1 Specifications of the sensors

Table 2 Detail components of the controller

Table 3 Average processing time of 10 executions