Advanced traffic video analytics for robust traffic accident detection

Automatic traffic accident detection is an important task in traffic video analysis due to its key applications in developing intelligent transportation systems. Reducing the time delay between the occurrence of an accident and the dispatch of the first responders to the scene may help lower the mortality rate and save lives. Since 1980, many approaches have been presented for the automatic detection of incidents in traffic videos. In this dissertation, some challenging problems for accident detection in traffic videos are discussed and a new framework is presented in order to automatically detect single-vehicle and intersection traffic accidents in real-time. First, a new foreground detection method is applied in order to detect the moving vehicles and subtract the ever-changing background in the traffic video frames captured by static or non-stationary cameras. For the traffic videos captured during day-time, the cast shadows degrade the performance of the foreground detection and road segmentation. A novel cast shadow detection method is therefore presented to detect and remove the shadows cast by moving vehicles and also the shadows cast by static objects on the road. Second, a new method is presented to detect the region of interest (ROI), which applies the location of the moving vehicles and the initial road samples and extracts the discriminating features to segment the road region. After detecting the ROI, the moving direction of the traffic is estimated based on the rationale that the crashed vehicles often make rapid change of direction. Lastly, single-vehicle traffic accidents and trajectory conflicts are detected using the first-order logic decision-making system. The experimental results using publicly available videos and a dataset provided by the New Jersey Department of Transportation (NJDOT) demonstrate the feasibility of the proposed methods. Additionally, the main challenges and future directions are discussed regarding (i) improving the performance of the foreground segmentation, (ii) reducing the computational complexity, and (iii) detecting other types of traffic accidents

An investigation into heavy vehicle drum brake squeal

Many mechanisms have been suggested for brake squeal over many years. In order to identify the most appropriate of these mechanisms, an experimental investigation has been carried out to define in detail the vibration characteristics of a squealing heavy vehicle air operated drum brake on both a vehicle and a laboratory brake test rig. This required the development of a novel 'scanning' technique for the modal analysis of the rotating drum, which showed the presence of well-defined complex wavelike modes. From these results, the dynamic behaviour of the drum, in particular, is found to be in good qualitative agreement with the predictions of a simple 'binary flutter' mechanism of squeal. Based on the role of rotor symmetry in this mechanism, a means of decoupling, flutter modes is developed involving a reduction in the rotational symmetry of the drum by means of attaching masses in a defined pattern at its periphery. It is shown theoretically that such decoupling would be expected to increase the dynamic stability of the brake, and experimental application of the technique confirms its effectiveness in reducing or eliminating squeal. Practical design aspects of reducing the rotational symmetry of the drum are considered, using finite element modelling, and it is also shown that the technique can be effective in other types of vehicle brake, such as disc brakes and hydraulic drum brakes. The simple lumped parameter models used in the above work are inadequate as brake design tools, however, and so a novel application of finite element modelling is used to extend the principle of the binary flutter mechanism to a more detailed model of a complete brake. This is shown to be capable of predicting known features of squeal and may be used as a brake design tool for both the brake structure and the friction material

Analysis of heat dissipation from railway and automotive friction brakes

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The thesis presents research into the understanding and improvement of heat dissipation from friction brakes. The investigations involved two brake types, considered to be the most thermally loaded and therefore most challenging; axle mounted high speed railway and commercial vehicle disc brakes. All three modes of heat transfer (conduction, convection and radiation) and airflow characteristics have been analysed experimentally and theoretically in order to increase the understanding of heat dissipation. Despite the very practical aspects of this research, a 'generic heat transfer approach' was applied, enabling wider engineering applications of the results. Experimental analyses conducted on a specially developed Spin Rig allowed measurements of cooling and airflow characteristics for different designs. Methodologies have been developed to determine thermal contact resistance, heat transfer coefficients, emissivity and aerodynamic (pumping) losses. Established values and relationships compared very favourably with theoretical work. Analytical, FE and CFD analyses were employed to further investigate design variations and perform sensitivity studies. Inertia dynamometer route simulations provided disc temperatures for validation of the overall work. Recommendations have been made for optimising heat dissipation, by proposing practically acceptable and economically viable design solutions. A proposed ventilated disc design efficiency ratio allows large, high speed ventilated disc designs, to be efficiently and accurately evaluated and compared, providing a valuable disc design optimisation tool. The determination of the methodologies, parameters and functions defining cooling characteristics, enable heat dissipation to be predicted confidently and accurately for brakes and other engineering assemblies at early design stages

Massive MIMO channel modelling for 5G wireless communication systems

Massive Multiple-Input Multiple-Output (MIMO) wireless communication systems, equipped with tens or even hundreds of antennas, emerge as a promising technology for the Fifth Generation (5G) wireless communication networks. To design and evaluate the performance of massive MIMO wireless communication systems, it is essential to develop accurate, flexible, and efficient channel models which fully reflect the characteristics of massive MIMO channels. In this thesis, four massive MIMO channel models have been proposed. First, a novel non-stationary wideband multi-confocal ellipse Two-Dimensional (2-D) Geometry Based Stochastic Model (GBSM) for massive MIMO channels is proposed. Spherical wavefront is assumed in the proposed channel model, instead of the plane wavefront assumption used in conventional MIMO channel models. In addition, the Birth-Death (BD) process is incorporated into the proposed model to capture the dynamic properties of clusters on both the array and time axes. Second, we propose a novel theoretical non-stationary Three-Dimensional (3-D) wideband twin-cluster channel model for massive MIMO communication systems with carrier frequencies in the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, nearfield effects instead of farfield effects are considered in the proposed model. These include the spherical wavefront assumption and a BD process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Third, a novel Kronecker Based Stochastic Model (KBSM) for massive MIMO channels is proposed. The proposed KBSM can not only capture antenna correlations but also the evolution of scatterer sets on the array axis. In addition, upper and lower bounds of KBSM channel capacities in both the high and low Signal-to-Noise Ratio (SNR) regimes are derived when the numbers of transmit and receive antennas are increasing unboundedly with a constant ratio. Finally, a novel unified framework of GBSMs for 5G wireless channels is proposed. The proposed 5G channel model framework aims at capturing key channel characteristics of certain 5G communication scenarios, such as massive MIMO systems, High Speed Train (HST) communications, Machine-to-Machine (M2M) communications, and Milli-meter Wave (mmWave) communications

Measurement, Modeling, and OFDM Synchronization for the Wideband Mobile-to-Mobile Channel

Wideband measurements of the mobile-to-mobile channel, especially of the harshest channels, are necessary for proper design and certification testing of mobile-to-mobile communications systems. A complete measurement implies that the Doppler and delay characteristics are measured jointly. However, such measurements have not previously been published. The main objective of the proposed research is to develop channel models for specific scenarios from data obtained in a wideband mobile-to-mobile measurement campaign in the 5.9 GHz frequency band. For this purpose we developed a channel sounding system including a novel combined waveform. In order to quantify and qualify either the recorded channel or the proposed generated channel, we developed a simulation test-bed that includes all the characteristics of the proposed digital short range communications (DSRC) standard. The resulting channel models needed to comply with the specifications required by hardware channel emulators or software channel simulators. From the obtained models, we selected one to be included in the IEEE 802.11p standard certification test. To further aid in the development of software radio based receivers, we also developed an orthogonal frequency division multiplexing (OFDM) synchronization algorithm to analyze and compensate synchronization errors produced by inaccessible system clocks.Ph.D.Committee Chair: Ingram, Mary Ann; Committee Member: Lanterman, Aaron; Committee Member: Li, Ye; Committee Member: Pratt, Thomas G.; Committee Member: Rogers, Peter H

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection