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Odongo Steven Eyobu(오돈고스티븐요부),Jhihoon Joo(주지훈),Dong Seog Han(한동석) 한국방송·미디어공학회 2015 한국방송공학회 학술발표대회 논문집 Vol.2015 No.7
In any mobile ad hoc environment, collision amongst mobile objects is always likely to occur unless there is a certain level of intelligence to detect and avoid the collision. This phenomenon of detection and avoidance is the key attribute for safety applications in vehicle to pedestrian (V2P) communications systems. In this paper, we propose a V2P communications concept for collision detection and avoidance.
Nassuna, Hellen,Kim, Jaehoon,Eyobu, Odongo Steven,Lee, Dongik Institute of Embedded Engineering of Korea 2020 대한임베디드공학회논문지 Vol.15 No.3
The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.