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Deaths due to road traffic accidents are one of the leading causes of death in the United States. Furthermore, the economic impact of road traffic accidents accounts for about 3% of the United States' annual gross domestic product (GDP). In the past decade, extensive research has focused on autonomous vehicles (AVs). This technology is said to help prevent traffic accidents while promoting road traffic safety. This study aims to investigate the safety performance of AVs and identify the significant risk factors associated with the AV collisions. The study considers more than 200 crashes involving AVs and includes vehicle factors, environmental factors, collision type and crash severity. Multinomial logistic regression was conducted with collision type. The results showed no statistically significant risk factors to crash severity. However, movement preceding to collision contributes significantly to collision type. When both vehicles are moving, there's a higher likelihood of an angled collision, 47% to be exact. The other scenario which demonstrates a high probability of an angled collision is when the AV vehicle is not moving while the other is moving. The highest probability for a rear-end collision to occur is when the AV vehicle is not moving while the other is moving. This scenario makes up 55% of the entire rear-end collisions. As for the second-highest proportion, both vehicles moving, it consists of 42%. The research shall help reduce AV involved collisions and increase driving safety. 

Ensuring maritime port security—a rapidly increasing concern in a post-9/11 world—presents certain operational challenges. As batteries and electric motors grow increasingly lighter and more powerful, unmanned aerial vehicles (UAVs) have been shown to be capable of enhancing a surveillance system’s capabilities and mitigating its vulnerabilities. In this paper, we looked at the current role unmanned systems are playing in port security and proposed an image-based method to enhance port security. The proposed method uses UAV real-time videos to detect and identify humans via human body detection and facial recognition. Experiments evaluated the system in real-time under differing environmental, daylight, and weather conditions. Three parameters were used to test feasibility: distance, height and angle. The findings suggest UAVs as an affordable, effective tool that may greatly enhance port safety and security. 

Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour. 

Air pollution is a major global risk to human health and environment. Particle matter (PM) with diameters less than 2.5 micrometers (PM2.5) is more harmful to human health than other air pollutants because it can penetrate deeply into lungs and damage human respiratory system. A new image-based deep feature analysis method is presented in this paper for PM2.5 concentration estimation. Firstly, low level and high level features are extracted from images and their spectrums by a deep learning neural network, and then regression models are created using the extracted deep features to estimate the PM2.5 concentrations, which are future refined by the collected weather information. The proposed method was evaluated using a PM2.5 dataset with 1460 photos and the experimental results demonstrated that our method outperformed other state-of-the-art methods.