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Aiming at the TTC (Time to Collision) and derivative indicators’ problems of unclear definition and missed/wrong judgments of traffic conflicts on curved road in the traditional Cartesian coordinate system, a new method that can better identify the conflicts on curved road is proposed. The method first establishes the Frenet coordinate system according to the road centerline (i.e., the reference line), and obtains the vehicle trajectory coordinates in the Frenet coordinate system. The Frenet coordinate system can simplify the calculation difficulty of vehicle trajectory and conflict under the curve road. Then determine the vehicle state in the Frenet coordinate system, and then use TTC to calculate rear-end and lane-change conflicts according to the state of the vehicle (non-lane-change/lane-change). Finally, a total of 4 hours of video data were collected based on the K283 of the lane-switch work zone of the Jiqing Highway. Subsequently, the continuous high-precision conflict data in the region was obtained through the video and conflict identification program, and the traditional method was compared with the new method. The results show that different methods have a significant impact on the identification of the number of serious conflicts. The new method can reduce the missed judgments of serious rear-end conflicts on curved road, especially at the junctions of curved and straight segments (segment 3/4/7/8/9), and can also reduce wrong judgments of serious lane-change conflicts. In addition, among the 125 added serious rear-end conflicts identified by the new method, the maximum deceleration of 10 conflicting vehicles during the conflict exceeds the dangerous state −4/-1.5m/s 2 , which explain that the new method can help us better identify the risks of curved road. The new method combines the Frenet coordinate system, vehicle state determination and TTC, which can reduce the missed/wrong judgments of conflicts on curved road, and expand the traffic conflict identification from previous straight road to full-line road alignment.

As a common method for identifying traffic risks, the traffic conflict technique needs to be more uniform in identifying different types of traffic conflicts and comparing the effectiveness of different conflict identification methods. To establish a unified identification method for traffic conflicts, this study describes the conditions under which vehicles collide through vehicle projection and constructs the Traffic Conflict of Unified Identification (TU) based on the time of vehicle collision to estimate the degree of risk of collision between vehicles. Using HighD high‐resolution traffic flow trajectory data with traffic conflict counts and cumulative exposure collision times, the differences and causes of different methods for all‐type and subtype conflict recognition were comparatively analyzed. From the perspective of a single traffic conflict, the correlation between conflict severity and the intensity of risk‐avoidance behaviors is proposed to compare and analyze the capability of the identification methods. The results show that (1) the unified identification method can better identify traffic conflicts without distinguishing the type of traffic conflicts, reducing the misjudgment of following conflicts and omission of lane changing conflicts to a greater extent, and being less affected by the predetermination of conflict types. (2) Compared with the traditional time to collision (TTC), TU has a better correlation between conflict severity and the intensity of vehicle‐avoidance behaviors, regardless of whether the conflict severity is characterized by the minimum collision time or the cumulative exposure collision time. The unified identification method improves the accuracy of traffic conflict identification, expands the scenario applicability of traffic conflict technology, and provides a new way for real‐time traffic conflict technology risk prediction for autonomous driving.

Background: Antibodies and derivative drugs targeting immune checkpoints have been approved for the treatment of several malignancies, but there are fewer responses in patients with pancreatic cancer. Here, we designed a nanobody molecule with bi-targeting on PD-L1 and CXCR4, as both targets are overexpressed in many cancer cells and play important roles in tumorigenesis. We characterized the biochemical and anti-tumour activities of the bispecific nanobodies in vitro and in vivo. Methods: A nanobody molecule was designed and constructed. The nanobody sequences targeting PD-L1 and CXCR4 were linked by the (G 4 S) 3 flexible peptide to construct the anti-PD-L1/CXCR4 bispecific nanobody. The bispecific nanobody was expressed in E. coli cells and purified by affinity chromatography. The purified nanobody was biochemically characterized by mass spectrometry, Western blotting and flow cytometry to confirm the molecule and its association with both PD-L1 and CXCR4. The biological function of the nanobody and its anti-tumour effects were examined by an in vitro tumour cell-killing assay and in vivo tumour inhibition in mouse xenograft models. Results: A novel anti-PD-L1/CXCR4 bispecific nanobody was designed, constructed and characterized. The molecule specifically bound to two targets on the surface of human cancer cells and inhibited CXCL12-induced Jurkat cell migration. The bispecific nanobody increased the level of IFN-γ secreted by T-cell activation. The cytotoxicity of human peripheral blood mononuclear cells (hPBMCs) against pancreatic cancer cells was enhanced by the molecule in combination with IL-2. In a human pancreatic cancer xenograft model, the anti-PD-L1/CXCR4 nanobody markedly inhibited tumour growth and was superior to the combo-treatment by anti-PD-L1 nanobody and anti-CXCR4 nanobody or treatment with atezolizumab as a positive control. Immunofluorescence and immunohistochemical staining of xenograft tumours showed that the anti-tumour effects were associated with the inhibition of angiogenesis and the infiltration of immune cells. Conclusion: These results clearly revealed that the anti-PD-L1/CXCR4 bispecific nanobody exerted anti-tumour efficacy in vitro and inhibited tumour growth in vivo. This agent can be further developed as a therapeutic reagent to treat human pancreatic cancer by simultaneously blocking two critical targets.

Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator Ti was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/Ti indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for Ti, which Ti indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved Ti can overcome these shortcomings; thus, Ti has the highest correlation. More data are needed to verify and support the study.

Vehicle trajectory data can reveal naturalistic driving behaviour trends. However, owing to measurement and processing errors, the trajectory data extracted from videos often contain obvious noise. In merging zones, vehicles tend to accelerate and decelerate frequently, leading to poor denoising performance of the linear Kalman filter (KF). To address this issue, this study proposes a new denoising method based on the adaptive Kalman filter, which automatically switches between KF and Unscented KF to accommodate car-following and merging behaviours, respectively. A merging behaviour detection method was designed based on the PELT method and normalized innovation squared (NIS). The F1 score of 92.9% shows the accuracy of behaviour detection. According to our results, the proposed method minimizes the range of jerk compared with other methods, reducing it from −4927.78 to 4960.72 of raw data to −44.92 to 47.14, indicating a significant improvement in denoising and trajectory smoothing. The goal of this study is to achieve high-precision trajectory data under complex real traffic scenarios.

Background and aimsReg4 is a recently discovered member of the regenerating gene family with distinctive expression profiles in primary cancers. To date, the physiological function of Reg4 is poorly understood. Previously, the authors found that Reg4 was markedly upregulated during acute pancreatitis (AP). The aim of this study was to investigate the role of Reg4 in experimental pancreatitis.MethodsAP was induced in C57BL/6 mice by administration of either l-arginine or caerulein, and Reg4 expression was assessed by immunofluorescence, reverse transcriptase (RT)-PCR and western blot analyses. Recombinant human Reg4 protein (rReg4), heat-inactivated Reg4, neutralising antibody and vehicle were also administered to mice by subcutaneous injection. The severity of AP was determined by measuring amylase and lipase activities in the serum and histological grading. The effect of rReg4 on cell death was examined and epidermal growth factor receptor (EGFR), p-EGFR, Akt, p-Akt, Bcl-2 and Bcl-xL expression were assessed by western blot analysis of isolated murine acinar cells treated with l-arginine.ResultsReg4 mRNA and protein were markedly upregulated during arginine-induced pancreatitis. Reg4 was widely expressed in residual acinar cells around the islets and regenerating metaplastic epithelium. rReg4 could protect against arginine-induced necrosis of acinar cells both in vivo and in vitro. This protective effect was also confirmed in the caerulein-induced murine model of AP. It was shown that arginine induced expression of Bcl-2 and Bcl-xL, while rReg4 upregulated Bcl-2 and Bcl-xL expression by activating the EGFR/Akt pathway. The upregulation of Bcl-xL correlated inversely with cell necrosis in isolated pancreatic acinar cells.ConclusionsThe data suggest that Reg4 may protect against acinar cell necrosis in experimental pancreatitis by enhancing the expression of Bcl-2 and Bcl-xL via activation of the EGFR/Akt signalling pathway.

Currently, many studies on the severity of traffic conflicts only considered the possibility of potential collisions but ignored the consequences severity of potential collisions. Aiming toward this defect, this study establishes a potential collision (serious conflict) consequences severity model on the basis of vehicle collision theory. Regional vehicles trajectory data and historical traffic accident data were obtained. The field data were brought into the conflict consequences severity model to calculate the conflict severity rate of each section under different TTC thresholds. For comparison, the traditional conflict rate of each section under different TTC thresholds that considered only the number of conflicts was also calculated. Results showed that the relationship between conflict severity rate and influencing factors was somehow different. The conflict severity rate seemed to have a higher correlation with accident rate and accident severity rate than conflict rate did. The TTC threshold value also affected the correlation between conflicts and accidents, with high and low TTC threshold indicating a lower correlation. The results showed that conflict severity rate that considered each single conflict consequence severity was a little better than the traditional conflict rate that considered only the numbers of conflicts in reflecting real risks as a new conflict evaluation indicator. The severity of traffic conflicts should consider two dimensions: the possibility and consequence of potential collisions. Based on this, we propose a new traffic safety evaluation method that takes into account the severity of the consequences of the conflict. More data and prediction models are needed to conduct more realistic and complex research in the future to ensure reliability of this new method.

PurposeThe main congestion on roads occur during peak hours, apart from incidents such as road accidents and construction works. Although there have been studies on peak period travels, these studies have only implicitly considered weekday, weekend and road type in their investigations. In this paper, it is proposed to investigate explicitly, the effect of weekday and weekend travel variability and road type on peak hour vehicular movement which leads to congestion. A study of vehicular movement patterns during these times can influence and impact on planning decisions for transportation engineers.MethodsThis study utilizes structural equation model (SEM) to investigate the vehicular movements influence of weekdays, weekends, road type choice and car type on two peak hour periods 6 am to 9 am and 4 pm to 7 pm and one off-peak hour 9 am to 12 noon.ResultsUsing vehicular movement data from Radio Frequency Identification for Nanjing, China, for the month of May 2014, it was revealed that in most of the cases, weekday travels influence peak hour travels more than weekends and that off-peak hour travels for both weekdays and weekends show little variations. The study also discovered that choice of road type and car type, have varying influence on peak hour travels.ConclusionsThe high significance ratios of results prove that these chosen variables are suitable for investigations into peak hour travel pattern studies. The study has also proved the viability of this modeling method to investigate policy measures to reduce peak period congestion.

The merge areas of freeway work zones include relatively significant safety hazards that have continuously led to urgent safety issues to be solved by the management departments. In order to make up for the cumbersome process of independent identification of rear-end collisions and lane change collisions on complex road sections, an appropriate identification method of traffic conflicts in the merge area of freeway work zone was explored, this study collected vehicle running tracking data from the merge areas of multiple work zones, using an unmanned aerial vehicle video technique. Based on an inter-frame difference method and the principle of a spatio-temporal context visual tracking algorithm, the vehicles were detected and tracked, and the coordinate data of the vehicles in continuous motion were parsed using MATLAB 2018b extension tools. Based on the behavior characteristics of vehicle conflict avoidance, a new identification method for evading severe traffic conflicts is proposed according to the initial velocity, acceleration, and accident rate of section traffic. Then, a statistical analysis was performed on the spatial distribution characteristics of the traffic conflicts in typical merge areas. The impacts of the road conditions in work zones, vehicle factors, and traffic flow factors on traffic conflicts were analyzed. A binomial logistic model was established to identify the main influencing factors. The results show that in the merge area of the freeway work zone, there are serious traffic conflicts between vehicles in the following two situations: (I) v∈[7,13.5] m/s and a∈[−3.96,−0.65] m/s2; and (Ⅱ) v∈[13.5,24.3] m/s, and a∈[−3.96,−1.57] m/s2. The probabilities of serious traffic conflicts in the first and last 25 m of the merge area are greater than those in the other sections. The smaller the space between the upstream work zone and the merge area, the greater the probability of serious traffic conflicts between vehicles. When the average vehicle speed is relatively high, the probability of serious conflicts is the highest, i.e., by a multiple of 5.95 from the baseline. Moreover, the probability of serious conflicts between vehicles is higher for larger vehicles, i.e., 4.765 times that for small vehicles. The research results can serve as a reference for freeway management departments to improve the safety levels of merge areas during road work. For example, the probability of serious conflicts can be effectively reduced by setting up reasonable speed limit signs in the work zone, increasing the spacing between the work zone and merge area, and appropriately diverting large vehicles.

The lack of vehicle feature information and the limited number of pixels in high-definition remote-sensing images causes difficulties in vehicle detection. This paper proposes U-YOLO, a vehicle detection method that integrates multi-scale features, attention mechanisms, and sub-pixel convolution. The adaptive fusion module (AF) is added to the backbone of the YOLO detection model to increase the underlying structural information of the feature map. Cross-scale channel attention (CSCA) is introduced to the feature fusion part to obtain the vehicle’s explicit semantic information and further refine the feature map. The sub-pixel convolution module (SC) is used to replace the linear interpolation up-sampling of the original model, and the vehicle target feature map is enlarged to further improve the vehicle detection accuracy. The detection accuracies on the open-source datasets NWPU VHR-10 and DOTA were 91.35% and 71.38%. Compared with the original network model, the detection accuracy on these two datasets was increased by 6.89% and 4.94%, respectively. Compared with the classic target detection networks commonly used in RFBnet, M2det, and SSD300, the average accuracy rate values increased by 6.84%, 6.38%, and 12.41%, respectively. The proposed method effectively solves the problem of low vehicle detection accuracy. It provides an effective basis for promoting the application of high-definition remote-sensing images in traffic target detection and traffic flow parameter detection.