No abstract

Short-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on. So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements. In this paper, a multiscale and high-precision LSTM-GASVR short-term traffic flow prediction algorithm is proposed. This method uses 15 min traffic flow data of the first 16 sections as input and completes the data preprocessing operation through reconstruction, normalization, and rising dimension by working day factor; establishing the prediction model based on the long- and short-term memory network (LSTM) and inverse normalization; and proposing the GA-SVR model to optimize the prediction results, so as to realize the real-time high-precision prediction of traffic flow. The prediction experiment is carried out according to the charge data of a toll station in Xi’an, Shaanxi Province, from May 2018 to May 2019. The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the LSTM, GRU, CNN, SAE, ARIMA, and SVR, and the R2 can reach 0.982, the explanatory variance is 0.982, and the MAPE is 0.118. The proposed traffic flow prediction algorithm provides strong support for traffic managers to judge the state of the road network to control traffic and guide traffic flow.

Vehicle dangerous behaviour warning plays an important role to improve road traffic safety and efficiency, so a safety assessment method of vehicle behaviour based on the improved Dempster–Shafer (D–S) evidence theory is proposed. Firstly, through analysis of vehicle collision accident mechanism, some factors closely related to vehicle safety are extracted. Also, multiple sensors are synthetically utilised to collect information, which realises the awareness of vehicle state, road attribute, driving environment etc. Then vehicle behaviour identification is accomplished according to the parameter information of the vehicle‐mounted sensors, as well as the related data of adjacent vehicles in vehicular ad hoc networks (VANET). Finally, a sequential type of weighted correction method based on evidence variance is used to integrate different levels of multi‐source heterogeneous information and to achieve safety assessment of vehicle behaviour. The experimental results show that the improved D–S evidence theory reduces the evidence conflict, increasing the accuracy and reliability of vehicle behaviour safety assessment. The study solves the fundamental core problem of active safety warning in VANET and provides a new means of traffic accident warning for the road traffic management department.

No abstract

No abstract

Purpose Precise vehicle localization is a basic and critical technique for various intelligent transportation system (ITS) applications. It also needs to adapt to the complex road environments in real-time. The global positioning system and the strap-down inertial navigation system are two common techniques in the field of vehicle localization. However, the localization accuracy, reliability and real-time performance of these two techniques can not satisfy the requirement of some critical ITS applications such as collision avoiding, vision enhancement and automatic parking. Aiming at the problems above, this paper aims to propose a precise vehicle ego-localization method based on image matching. Design/methodology/approach This study included three steps, Step 1, extraction of feature points. After getting the image, the local features in the pavement images were extracted using an improved speeded up robust features algorithm. Step 2, eliminate mismatch points. Using a random sample consensus algorithm to eliminate mismatched points of road image and make match point pairs more robust. Step 3, matching of feature points and trajectory generation. Findings Through the matching and validation of the extracted local feature points, the relative translation and rotation offsets between two consecutive pavement images were calculated, eventually, the trajectory of the vehicle was generated. Originality/value The experimental results show that the studied algorithm has an accuracy at decimeter-level and it fully meets the demand of the lane-level positioning in some critical ITS applications.

For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

No abstract