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Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

Motor-vehicle-related events (MVEs) are the leading cause of on-duty death for law enforcement officers, yet little is known about how officers view this significant job hazard. The purpose of this paper is to explore officers’ motor-vehicle risk perception and examine how prior on-duty MVEs and the death or injury of a fellow officer influences this perception.

A state-wide random sample of 136 law enforcement agencies was drawn using publically accessible databases, stratified on type and size of agency. In total, 60 agencies agreed to participate and a cross-sectional questionnaire was distributed to 1,466 officers. Using six-point Likert scales, composite scores for motor-vehicle and intentional violence risk perception were derived. A linear regression multivariable model was used to examine factors affecting motor-vehicle risk perception.

Motor-vehicle risk perception scores were significantly higher than intentional violence scores. A prior on-duty motor-vehicle crash, prior roadside incident, or knowledge of fellow officer’s injury or death from a MVE significantly increased motor-vehicle risk perception scores. After controlling for potential confounders though, only prior on-duty crashes and roadside incidents impacted motor-vehicle risk perception.

The study comprised primarily small, rural agencies and generalizability may be limited. Also, although the data were collected anonymously, reporting and response biases may affect these findings.

This study involved a large and diverse cohort of officers and explored motor-vehicle risk perception. A better understanding of officers’ risk perceptions will assist in the development and implementation of occupational injury prevention programs, training, and policy.

This study first aimed to investigate the differences in drug driver detection rates between a trial of randomised and targeted enforcement operations. The second aim was to identify which indicator categories are most commonly used by police to target drug drivers and to assess the effectiveness of targeted drug testing. Finally, this study aimed to quantify what specific indicators and cues (of the overarching categories) triggered their decision to drug test drivers and which indicators were most successful.

This research examined the detection rates in a trial comparison of randomised and targeted roadside drug testing (RDT) operations as well as the methods utilised by police in the targeted operations to identify potential drug driving offenders.

Visual appearance was by far the most commonly utilised indicator followed by age, police intelligence on prior charges, vehicle appearance and behavioural cues. However, the use of police intelligence was identified as the most successful indicator that correlated with positive oral fluid testing results. During the randomised RDT operations, 3.4% of all drivers who were tested yielded a positive roadside oral fluid result compared to 25.5% during targeted RDT operations.

The targeted RDT approach, while determined to be an effective detection methodology, limits the overall deterrent effect of roadside testing in a more general driving population, and the need for a balanced approach to ensure detection and deterrence is required. This study highlights that by focussing on night times for randomised RDT operations and the identified effective indicators for targeted operations, an effective balance of deterrence and detection could be achieved.

While the presence of a single indicator is not indicative of a drug driver, this study highlights for police which indicators currently used are more effective at detecting a drug driver. As a result, police could adapt current RDT procedures to focus on the presence of these indicators to support drug driver detection.

This is a world-first study that examines both randomised and targeted roadside drug testing. This study controls for location and time of day while using the same police unit for roadside testing, thus is able to make direct comparisons between the two methodologies to determine the effectiveness of police targeting for roadside drug testing. Furthermore, this study highlights which indicators used by police results in the highest rate of positive roadside drug tests.

Pabna is suffering from waterlogging problems from ancient times. The previous Drainage Master Plan was failed due to the lack of reflection of the general people of Pabna Municipality. As a result, this study focuses on identifying the causes of waterlogging in people's eyes. This paper will help the local authority and planners eradicate the waterlogging problem and build a planned and resilient community.

Both primary and secondary data were used for the study. Present drainage pattern, topology climatic elements were collected from the journals, websites and Municipal Ingratiated Development Plan (MIDP), Pabna-2008. The sample size was 246, and this respondent was surveyed. By the survey, the people's perception of waterlogging was collected. For that, five independent variables and one dependent variable were determined. These variables were determined by previous studies, reconnaissance survey of the study area. It used multiple linear regression and the correlation method; the causes of waterlogging were determined.

The study found solid waste disposal into the drainage, absence of operation and maintenance system, small discharge capacity with blocked in the current drains, nonappearance of combined drainage network of roadside drains and unplanned drainage system as the leading causes of waterlogging from the perception of the people. Also found that the absence of operation and maintenance system, solid waste disposal into the drainage and unplanned drainage system as the influencing causes on small discharge capacity with blocked in the current drains.

This study has focused on people's perceptions rather than secondary data. That is why this study will significantly impact eradicating the waterlogging problem from the Pabna Municipality and will carry out the core problems without any bias. This will lead to a sustainable, planned and resilient community.

The online users’ characteristic information can provide decision support for policy-designing and construction of public strategies. Hence, this paper aims to conduct online public opinion mining on the recovery policy stimulating the economies stroked by COVID-19 epidemic. Also, sentimental analysis is performed to uncover the posters’ emotion towards the target policy.

This paper adopts bidirectional encoder representations from transformers (BERT) as classifier in classification tasks, including misinformation detection, subject analysis and sentimental analysis. Meanwhile, latent Dirichlet allocation method and sentiment formulations are implemented in topic modelling and sentiment analysis.

The experimental results indicate that public opinion is mainly non-negative to the target policy. The positive emotions mainly focus on the benefits that the recovery policy might bring to stimulate economy. On the other hand, some negative opinions concerned about the shortcomings and inconvenience of the target policy.

The authors figured out the key factors focused by the public opinion on the target recovery policy. Also, the authors indicated pros and cons of the recovery policy by analysing the emotion and the corresponding topics of the public opinion on social media. The findings of the paper can be generalized in other countries theoretically to help them design recovery policy against COVID-19.