지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.11
- 수록면
- 45 - 48 (4page)
이용수
초록· 키워드
오류제보하기
The vehicle positioning system is an essential component for developing an autonomous vehicle. Highly accurate and reliable position has to be updated frequently for driving a vehicle autonomously. However, for positioning the vehicle based on a global positioning system (GPS) alone is not accurate due to the GPS blockage and a multipath error. In order to overcome this problem, the sensor fusion of the position data from GPS with in-vehicle sensors has been used widely. In general, a single model filter is frequently used for proper positioning of the vehicle, but it is not suitable for various driving conditions. For this reason, a multiple-model filter has been studied to apply in a wide-range of driving conditions. The multiple-model filter can apply in various conditions because it combines several models using the model probability. In this study, we proposed a neural network based multiple-model filter using the GPS with in-vehicle sensors. The model probability can be obtained by using the off-line learning of the neural network. The proposed multiple-model filter was verified by simulation using a commercial vehicle model. The simulation results show that the proposed multiple-model filter is capable of adapting to various driving conditions.
목차
Abstract
1. INTRODUCTION
2. VEHICLE MODELS
3. MULTIPLE-MODEL FILTER
4. SIMULATION
5. CONCLUSION
References
참고문헌 (0)
참고문헌 신청이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2013-556-001412160