Drive Video Analysis for the Detection of Traffic Near-Miss Incidents

Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection).Comment: Accepted to ICRA 201

Video analytics on the MLK Smart Corridor testbed

With the predicted boom of urban environment populations in the next 30 years, many new challenges in urban transportation will surface. In an effort to mitigate these, the Center for Urban Informatics and Progress (CUIP) has been introduced along with its testbed. One opportunity this testbed provides is the ability to utilize computer vision and video analytics to anonymously gather data on how citizens traverse the city. This thesis shall discuss an approach to real-time object tracking that serves as a basis for further analytics such as traffic flow data collection and near-miss detection. The proposed video analytics platform will aid citizens with their day-to-day commute through the corridor by deriving real-time data based on actual behavior seen in the citizens\u27 commute. Furthermore, since the testbed is ever-expanding in both hardware and size the algorithms and software proposed in this thesis are designed to prioritize scalability

Cycling near misses: A review of the current methods, challenges and the potential of an AI-embedded system

Whether for commuting or leisure, cycling is a growing transport mode in many countries. However, cycling is still perceived by many as a dangerous activity. Because the mode share of cycling tends to be low, serious incidents related to cycling are rare. Nevertheless, the fear of getting hit or falling while cycling hinders its expansion as a transport mode and it has been shown that focusing on killed and seriously injured casualties alone only touches the tip of the iceberg. Compared with reported incidents, there are many more incidents in which the person on the bike was destabilised or needed to take action to avoid a crash; so-called near misses. Because of their frequency, data related to near misses can provide much more information about the risk factors associated with cycling. The quality and coverage of this information depends on the method of data collection; from survey data to video data, and processing; from manual to automated. There remains a gap in our understanding of how best to identify and predict near misses and draw statistically significant conclusions, which may lead to better intervention measures and the creation of a safer environment for people on bikes. In this paper, we review the literature on cycling near misses, focusing on the data collection methods adopted, the scope and the risk factors identified. In doing so, we demonstrate that, while many near misses are a result of a combination of different factors that may or may not be transport-related, the current approach of tackling these factors may not be adequate for understanding the interconnections between all risk factors. To address this limitation, we highlight the potential of extracting data using a unified input (images/videos) relying on computer vision methods to automatically extract the wide spectrum of near miss risk factors, in addition to detecting the types of events associated with near misses

An Improved Object Detection and Trajectory Prediction Method for Traffic Conflicts Analysis

Although computer vision-based methods have seen broad utilisation in evaluating traffic situations, there is a lack of research on the assessment and prediction of near misses in traffic. In addition, most object detection algorithms are not very good at detecting small targets. This study proposes a combination of object detection and tracking algorithms, Inverse Perspective Mapping (IPM), and trajectory prediction mechanisms to assess near-miss events. First, an instance segmentation head was proposed to improve the accuracy of the object frame box detection phase. Secondly, IPM was applied to all detection results. The relationship between them is then explored based on their distance to determine whether there is a near-miss event. In this process, the moving speed of the target was considered as a parameter. Finally, the Kalman filter is used to predict the object\u27s trajectory to determine whether there will be a near-miss in the next few seconds. Experiments on Closed-Circuit Television (CCTV) datasets showed results of 0.94 mAP compared to other state-of-the-art methods. In addition to improved detection accuracy, the advantages of instance segmentation fused object detection for small target detection are validated. Therefore, the results will be used to analyse near misses more accurately

The ‘frontal lobe’ project: A double-blind, randomized controlled study of the effectiveness of higher level driving skills training to improve frontal lobe (executive) function related driving performance in young drivers

The current study was undertaken in order to evaluate the effectiveness of higher level skills training on safe driving behaviour of 36 teenage drivers. The participants, who attended the Driver Training Research camp in Taupo (NZ) over a two week period, were 16 to 17 years old and had a valid restricted driver licence. The study focused on four main aims. Firstly, the behavioural characteristics of the sample and their attitudes to risk taking and driving were examined. Results showed that speeding was the most anticipated driving violation, and high levels of confidence were associated with a higher number of crashes and a greater propensity for risk taking. Many, often male participants, also rated their driving skills as superior to others and thought they would be less likely than others to be involved in an accident. Secondly, the relationship between driving performance and executive functioning, general ability and sustained attention was evaluated. Overall, better driving performance and more accurate self-evaluation of driving performance was related to higher levels of executive functions, in particular, working memory, and cognitive switching. In addition, higher general ability and greater ability to sustain attention were also linked to better performance on the driving related assessments. The third focus of this study was to compare the effects of both, higher level and vehicle handling skills training on driving performance, confidence levels and attitudes to risk. While both types of training improved direction control, speed choice and visual search, along with number of hazards detected and actions in relation to hazards, statistically significant improvement on visual search was seen only after higher level skills training. Vehicle handling skills training significantly improved direction control and speed choice. In addition, confidence levels in their driving skills were significantly lowered and attitudes to speeding, overtaking and close following had improved significantly in the participants after the higher level driving skills training. The final aspect to this study was to examine the effects of the training over the following 6 month period based on self-reported driving behaviour. The response rate of participants however, was not sufficient to reach any meaningful conclusion on any long-term training effects. A pilot study using GPSbased data trackers to assess post-training driving behaviour revealed some promising results for future driver training evaluation studies. The overall implications of the results are discussed in relation to improving the safety of young drivers in New Zealand

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces

Automated Accident Detection In Intersections Via Digital Audio Signal Processing

The aim of this thesis is to design a system for automated accident detection in intersections. The input to the system is a three-second audio signal. The system can be operated in two modes: two-class and multi-class. The output of the two-class system is a label of ?crash? or ?non-crash?. In the multi-class system, the output is the label of ?crash? or various non-crash incidents including ?pile drive?, ?brake?, and ?normal-traffic? sounds. The system designed has three main steps in processing the input audio signal. They are: feature extraction, feature optimization and classification. Five different methods of feature extraction are investigated and compared; they are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstrum transform and Mel frequency cepstral transform. Linear discriminant analysis (LDA) is used to optimize the features obtained in the feature extraction stage by linearly combining the features using different weights. Three types of statistical classifiers are investigated and compared: the nearest neighbor, nearest mean, and maximum likelihood methods. Data collected from Jackson, MS and Starkville, MS and the crash signals obtained from Texas Transportation Institute crash test facility are used to train and test the designed system. The results showed that the wavelet based feature extraction method with LDA and maximum likelihood classifier is the optimum design. This wavelet-based system is computationally inexpensive compared to other methods. The system produced classification accuracies of 95% to 100% when the input signal has a signal-to-noise-ratio of at least 0 decibels. These results show that the system is capable of effectively classifying ?crash? or ?non-crash? on a given input audio signal