Object Detection in Adaptive Cruise Control Using Multi-Class Support Vector Machine

被引：0

作者：

Park, Hyun Soo ^{[1
]}

Kim, Dae Jung ^{[1
]}

Kang, Chang Mook ^{[1
]}

Kee, Seok Cheol ^{[2
]}

Chung, Chung Choo ^{[3
]}

机构：

[1] Hanyang Univ, Dept Elect Engn, Seoul 04763, South Korea

[2] Chunghuk Natl Univ, Smart Car Res Ctr, Cheongju 28644, South Korea

[3] Hanyang Univ, Div Elect & Biomed Engn, Seoul 04763, South Korea

来源：

2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | 2017年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a new object detection method in Adaptive Cruise Control (ACC) with the Support Vector Machine (SVM) algorithm using data from a radar system. ACC using Closest in Path Vehicle (CIPV) detects the object vehicle that comes in front of the vehicle's front radar. Therefore, if the object vehicle abruptly cuts into the lane ahead of ego vehicle, the speed of the ego vehicle should quickly reduce. This phenomenon makes passengers feel uncomfortable. To cope with this phenomenon, in this paper we propose multiple classifications of various driving situations using multi-class SVM. Classified data was used to detect the CIPV among nearby vehicles in ACC. The proposed method shows improved performance in predicting the motion of objects in advance over the conventional radar system so that it enable for the ACC system either to decelerate or to accelerate smoothly in advance. The performance of proposed method was validated via experimental results.

引用

页数：6

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