Study on Leading Vehicle Detection at Night Based on Multisensor and Image Enhancement Method

Low visibility is one of the reasons for rear accident at night. In this paper, we propose a method to detect the leading vehicle based on multisensor to decrease rear accidents at night. Then, we use image enhancement algorithm to improve the human vision. First, by millimeter wave radar to get the world coordinate of the preceding vehicles and establish the transformation of the relationship between the world coordinate and image pixels coordinate, we can convert the world coordinates of the radar target to image coordinate in order to form the region of interesting image. And then, by using the image processing method, we can reduce interference from the outside environment. Depending on D-S evidence theory, we can achieve a general value of reliability to test vehicles of interest. The experimental results show that the method can effectively eliminate the influence of illumination condition at night, accurately detect leading vehicles, and determine their location and accurate positioning. In order to improve nighttime driving, the driver shortage vision, reduce rear-end accident. Enhancing nighttime color image by three algorithms, a comparative study and evaluation by three algorithms are presented. The evaluation demonstrates that results after image enhancement satisfy the human visual habits

Unsupervised video anomaly detection in UAVs: a new approach based on learning and inference

In this paper, an innovative approach to detecting anomalous occurrences in video data without supervision is introduced, leveraging contextual data derived from visual characteristics and effectively addressing the semantic discrepancy that exists between visual information and the interpretation of atypical incidents. Our work incorporates Unmanned Aerial Vehicles (UAVs) to capture video data from a different perspective and to provide a unique set of visual features. Specifically, we put forward a technique for discerning context through scene comprehension, which entails the construction of a spatio-temporal contextual graph to represent various aspects of visual information. These aspects encompass the manifestation of objects, their interrelations within the spatio-temporal domain, and the categorization of the scenes captured by UAVs. To encode context information, we utilize Transformer with message passing for updating the graph's nodes and edges. Furthermore, we have designed a graph-oriented deep Variational Autoencoder (VAE) approach for unsupervised categorization of scenes, enabling the extraction of the spatio-temporal context graph across diverse settings. In conclusion, by utilizing contextual data, we ascertain anomaly scores at the frame-level to identify atypical occurrences. We assessed the efficacy of the suggested approach by employing it on a trio of intricate data collections, specifically, the UCF-Crime, Avenue, and ShanghaiTech datasets, which provided substantial evidence of the method's successful performance

Privacy preservation via beamforming for NOMA

Non-orthogonal multiple access (NOMA) has been proposed as a promising multiple access approach for 5G mobile systems because of its superior spectrum efficiency. However, the privacy between the NOMA users may be compromised due to the transmission of a superposition of all users’ signals to successive interference cancellation (SIC) receivers. In this paper, we propose two schemes based on beamforming optimization for NOMA that can enhance the security of a specific private user while guaranteeing the other users’ quality of service (QoS). Specifically, in the first scheme, when the transmit antennas are inadequate, we intend to maximize the secrecy rate of the private user, under the constraint that the other users’ QoS is satisfied. In the second scheme, the private user’s signal is zero-forced at the other users when redundant antennas are available. In this case, the transmission rate of the private user is also maximized while satisfying the QoS of the other users. Due to the nonconvexity of optimization in these two schemes, we first convert them into convex forms and then, an iterative algorithm based on the ConCave-Convex Procedure is proposed to obtain their solutions. Extensive simulation results are presented to evaluate the effectiveness of the proposed scheme

Botulinum Neurotoxin D Uses Synaptic Vesicle Protein SV2 and Gangliosides as Receptors

Botulinum neurotoxins (BoNTs) include seven bacterial toxins (BoNT/A-G) that target presynaptic terminals and act as proteases cleaving proteins required for synaptic vesicle exocytosis. Here we identified synaptic vesicle protein SV2 as the protein receptor for BoNT/D. BoNT/D enters cultured hippocampal neurons via synaptic vesicle recycling and can bind SV2 in brain detergent extracts. BoNT/D failed to bind and enter neurons lacking SV2, which can be rescued by expressing one of the three SV2 isoforms (SV2A/B/C). Localization of SV2 on plasma membranes mediated BoNT/D binding in both neurons and HEK293 cells. Furthermore, chimeric receptors containing the binding sites for BoNT/A and E, two other BoNTs that use SV2 as receptors, failed to mediate the entry of BoNT/D suggesting that BoNT/D binds SV2 via a mechanism distinct from BoNT/A and E. Finally, we demonstrated that gangliosides are essential for the binding and entry of BoNT/D into neurons and for its toxicity in vivo, supporting a double-receptor model for this toxin

Human-like Attention-Driven Saliency Object Estimation in Dynamic Driving Scenes

Identifying a notable object and predicting its importance in front of a vehicle are crucial for automated systems’ risk assessment and decision making. However, current research has rarely exploited the driver’s attentional characteristics. In this study, we propose an attention-driven saliency object estimation (SOE) method that uses the attention intensity of the driver as a criterion for determining the salience and importance of objects. First, we design a driver attention prediction (DAP) network with a 2D-3D mixed convolution encoder–decoder structure. Second, we fuse the DAP network with faster R-CNN and YOLOv4 at the feature level and name them SOE-F and SOE-Y, respectively, using a shared-bottom multi-task learning (MTL) architecture. By transferring the spatial features onto the time axis, we are able to eliminate the drawback of the bottom features being extracted repeatedly and achieve a uniform image-video input in SOE-F and SOE-Y. Finally, the parameters in SOE-F and SOE-Y are classified into two categories, domain invariant and domain adaptive, and then the domain-adaptive parameters are trained and optimized. The experimental results on the DADA-2000 dataset demonstrate that the proposed method outperforms the state-of-the-art methods in several evaluation metrics and can more accurately predict driver attention. In addition, driven by a human-like attention mechanism, SOE-F and SOE-Y can identify and detect the salience, category, and location of objects, providing risk assessment and a decision basis for autonomous driving systems

Effects of Ticket-Checking Failure on Dynamics of Pedestrians at Multi-Exit Inspection Points with Various Layouts

It is of great significance to understand the dynamics and risk level of pedestrians at the multi-exit inspection points, since they are the bottlenecks of pedestrian flow leaving public places, such as subway and railway stations. Microscopic simulations were carried out with a cellular automata model to investigate the effects of ticket-checking failure on pedestrian dynamics when passing through the multi-exit inspection points with parallel, convex and concave layouts. It was found that although ticket-checking failure could reduce the passing efficiency, it also lowers the competitive level between pedestrians and enhances passing safety in the range of medium and high pedestrian density. The competitive level decreases when increasing the probability of ticket-checking failure and the corresponding delay. The probability of ticket-checking failure and the corresponding delay have equivalent effects on passing efficiency and safety, and can be integrated as average delay. A fitted equation was proposed for the dependence of passing efficiency and safety on average delay. With the existence of ticket-checking failure in reality, the concave layout of the multi-exit inspection points gives rise to a much lower competitive level compared with the parallel and convex ones, which would enhance the safety of pedestrians at the exits

Human-like Attention-Driven Saliency Object Estimation in Dynamic Driving Scenes

Identifying a notable object and predicting its importance in front of a vehicle are crucial for automated systems’ risk assessment and decision making. However, current research has rarely exploited the driver’s attentional characteristics. In this study, we propose an attention-driven saliency object estimation (SOE) method that uses the attention intensity of the driver as a criterion for determining the salience and importance of objects. First, we design a driver attention prediction (DAP) network with a 2D-3D mixed convolution encoder–decoder structure. Second, we fuse the DAP network with faster R-CNN and YOLOv4 at the feature level and name them SOE-F and SOE-Y, respectively, using a shared-bottom multi-task learning (MTL) architecture. By transferring the spatial features onto the time axis, we are able to eliminate the drawback of the bottom features being extracted repeatedly and achieve a uniform image-video input in SOE-F and SOE-Y. Finally, the parameters in SOE-F and SOE-Y are classified into two categories, domain invariant and domain adaptive, and then the domain-adaptive parameters are trained and optimized. The experimental results on the DADA-2000 dataset demonstrate that the proposed method outperforms the state-of-the-art methods in several evaluation metrics and can more accurately predict driver attention. In addition, driven by a human-like attention mechanism, SOE-F and SOE-Y can identify and detect the salience, category, and location of objects, providing risk assessment and a decision basis for autonomous driving systems