Development of real-time forward collision warning system using artificial neural network = 인공신경망을 활용한 실시간 전방 추돌 경고 시스템 개발

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Most of the existing forward Collision Warning Systems (CWS) that involve the variable Perception-Reaction Time (PRT) have some negative effects on the collision warning performance due to the poor adaptive capability for the influence of different human PRTs. In an effort to address such issues associated with the parametric approaches, there has been a growing interest to develop a novel Artificial Neural Network (ANN)-based forward CWS in the field of Advanced Driver Assistance System (ADAS). However, since the previous studies show limitations for real-time learning to adjust and update pre-determined weights by considering spatiotemporal traffic pattern, they are of doubtful validity in the context of real-time forward CWS. This research develops a real-time learning rule called Rolling Period (RP)-based real-time learning algorithm that enables ANN-based forward CWSs to adjust their current weights depending on the real-time traffic information. With the real-time learning rule, Multi-layer Perceptron Neural Network-based forward CWS (MCWS) is proposed. The MCWS provides real-time forward collision warning by predicting potential critical deceleration in a subsequent few seconds. Comparison study demonstrates that the MCWS significantly reduces the number of nuisance and missed alarms compared to other conventional parametric forward CWSs, including Honda and Berkeley system, without any influence of the human PRT. To further enhance the performance of the MCWS, this study proposes Real-time associative memory-based forward CWS (RCWS), which updates and adjusts the pre-determined weights by using real-time sectional traffic information from vehicle sensor and smartphone in a cloud computing environment. Numerical analysis shows that the RCWS not only provides better warning performance compared to the MCWS, but also yields a significant improvement in the warning performance with a relatively low Market Penetration Rate (MPR) of in-vehicle sensors and communication devices. As an extension of the previous study on the RCWS, modified RCWSs using Multi-layer Perceptron Neural Network with two hidden layers and Radial Basis Function Network (RBFN) are proposed to mitigate impact of Communication Delay (CD) between smartphone and cloud server on the warning performance. Numerical study reveals that the overall performance improvements of the modified RCWSs compared to the original RCWS increases as the CD increases, which implies that the benefit of using the modified RCWSs increases as the time delay of uploading individual traffic data from the smartphone to the cloud server increases.
Advisors
Yeo, Hwasooresearcher여화수researcher
Description
한국과학기술원 :건설및환경공학과,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 건설및환경공학과, 2018.2,[viii, 124 p. :]

Keywords

forward collision warning system▼aperception-reaction time▼aartificial neural network▼aadvanced driver assistance system▼areal-time learning; 인지반응시간▼a전방 추돌 경고 시스템▼a인공신경망▼a첨단운전자지원시스템▼a실시간 학습

URI
http://hdl.handle.net/10203/264398
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=734223&flag=dissertation
Appears in Collection
CE-Theses_Ph.D.(박사논문)
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