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<p>Aquaplaning accidents are relatively rare, but could have fatal effects. The task of this master’s thesis is to use data from the Laser Road Surface Tester to detect road sections with risk of aquaplaning. </p><p>A three-dimensional model based on data from road surface measurements is created using MATLAB (version 6.1). From this general geometrical model of the road, a pond model is produced from which the theoretical risk ponds are detected. A risk pond indication table is fur-ther created. </p><p>The pond model seems to work well assuming that the data from the road model is correct. Determining limits for depth and length of risk ponds can be made directly by the user. MATLAB code is reasonably easy to understand and this leaves great opportunities for changing different parameters in a simple way. </p><p>Supplementary research is needed to further improve the risk pond detection model. Collecting data at smaller intervals and with more measurement points would be desirable for achieving better correlation with reality. In a future perspective, it would be wise to port the code to another programming language and this could make the computations faster.</p>

Aquaplaning accidents are relatively rare, but could have fatal effects. The task of this master’s thesis is to use data from the Laser Road Surface Tester to detect road sections with risk of aquaplaning. A three-dimensional model based on data from road surface measurements is created using MATLAB (version 6.1). From this general geometrical model of the road, a pond model is produced from which the theoretical risk ponds are detected. A risk pond indication table is fur-ther created. The pond model seems to work well assuming that the data from the road model is correct. Determining limits for depth and length of risk ponds can be made directly by the user. MATLAB code is reasonably easy to understand and this leaves great opportunities for changing different parameters in a simple way. Supplementary research is needed to further improve the risk pond detection model. Collecting data at smaller intervals and with more measurement points would be desirable for achieving better correlation with reality. In a future perspective, it would be wise to port the code to another programming language and this could make the computations faster.

Modern cars are often equipped with sensors like radar, infrared cameras and stereo cameras that collect information about its surroundings. By using a stereo camera, it is possible to receive information about the distance to points in front of the car. This information can be used to estimate the height of the predicted path of the car. An application which does this is the stereo based Road surface preview (RSP) algorithm. By using the output from the RSP algorithm it is possible to use active suspension control, which controls the vertical movement of the wheels relative to the chassis. This application primarily makes the driving experience more comfortable, but also extends the durability of the vehicle. The idea behind this Master’s thesis is to create an evaluation tool for the RSP algorithm, which can be used at arbitrary roads.  The thesis describes the proposed evaluation tool, where focus has been to make an accurate comparison of camera data received from the RSP algorithm and laser data used as ground truth in this thesis. Since the tool shall be used at the company proposing this thesis, focus has also been on making the tool user friendly. The report discusses the proposed methods, possible sources to errors and improvements. The evaluation tool considered in this thesis shows good results for the available test data, which made it possible to include an investigation of a possible improvement of the RSP algorithm.

The research explores aspects of road surface measurement and monitoring, targeting some of the main challenges in the field, including cost and portability of high-speed inertial profilers. These challenges are due to the complexities of modern profilers to integrate various sensors while using advanced algorithms and processes to analyse measured sensor data. Novel techniques were proposed to improve the accuracy of road surface longitudinal profiles using inertial profilers. The thesis presents a Half-Wavelength Peak Matching (HWPM) model, designed for inertial profilers that integrate a laser displacement sensor and an accelerometer to evaluate surface irregularities. The model provides an alternative approach to drift correction in accelerometers, which is a major challenge when evaluating displacement from acceleration. The theory relies on using data from the laser displacement sensor to estimate a correction offset for the derived displacement. The study also proposes an alternative technique to evaluating vibration velocity, which improves on computational factors when compared to commonly used methods. The aim is to explore a different dimension to road roughness evaluation, by investigating the effect of surface irregularities on vehicle vibration. The measured samples show that the drift in the displacement calculated from the accelerometer increased as the vehicle speed at which the road measurement was taken increased. As such, the significance of the HWPM model is more apparent at higher vehicle speeds, where the results obtained show noticeable improvements to current techniques. All results and analysis carried out to validate the model are based on real-time data obtained from an inertial profiler that was designed and developed for the research. The profiler, which is designed for portability, scalability and accuracy, provides a Power Over Ethernet (POE) enabled solution to cope with the demand for high data transmission rates.

Transportation networks play a significant role in the economy of Canadians during winter seasons; thus, maintaining a safe and economic flow of traffic on Canadian roads is crucial. Winter contaminants such as freezing rain, snow, and ice cause reduced friction between vehicle tires and pavement and thus increased accident-risk and decreased road capacity. The formation of ice and frost caused by snowfall and wind chill makes driving a very difficult task. Pavement surface temperature is an important indicator for road authorities when they are deciding the optimal time to apply anti-icer/deicer chemicals and when estimating their effect and the optimal amounts to apply. By forecasting pavement temperature, maintenance crews can figure out road surface conditions ahead of time and start their operations in a timely manner, thereby reducing salt use and increasing the safety and security of road users by eliminating accidents caused by slipperiness. This research investigates the feasibility of applying simple statistical models for forecasting road surface temperatures at locations where RWIS data are available. Two commonly used modeling techniques were considered: time-series analysis and artificial neural networks (ANN). A data set from an RWIS station is used for model calibration and validation. The analysis indicates that multi-variable SARIMA is the most competitive technique and has the lowest number of forecasting errors.

Transportation networks play a significant role in the economy of Canadians during winter seasons; thus, maintaining a safe and economic flow of traffic on Canadian roads is crucial. Winter contaminants such as freezing rain, snow, and ice cause reduced friction between vehicle tires and pavement and thus increased accident-risk and decreased road capacity. The formation of ice and frost caused by snowfall and wind chill makes driving a very difficult task. Pavement surface temperature is an important indicator for road authorities when they are deciding the optimal time to apply anti-icer/deicer chemicals and when estimating their effect and the optimal amounts to apply. By forecasting pavement temperature, maintenance crews can figure out road surface conditions ahead of time and start their operations in a timely manner, thereby reducing salt use and increasing the safety and security of road users by eliminating accidents caused by slipperiness. This research investigates the feasibility of applying simple statistical models for forecasting road surface temperatures at locations where RWIS data are available. Two commonly used modeling techniques were considered: time-series analysis and artificial neural networks (ANN). A data set from an RWIS station is used for model calibration and validation. The analysis indicates that multi-variable SARIMA is the most competitive technique and has the lowest number of forecasting errors.

No abstract is provided. / Thesis / Master of Engineering (MEngr) / Scope and contents: This thesis describes an analytical study of the dynamics of a tractor-semitrailer vehicle. Two mathematical models; an articulated vehicle with self steering semitrailer and an articulated vehicle with a standard semitrailer, are developed to describe the longitudinal, lateral, vertical, pitching, rolling and yawing motions of the vehicle on a rough road surface. The natural frequencies of and the damped eigenvalues for both models are calculated. The steady state response of the vehicle components to the sinusoidal input profile of varying frequencies is calculated and the response curves are computer plotted in each case. For the self-steering semitrailer, the effect of varying the spring stiffness at the fifth wheel is studied. The dynamic loads imparted to the pavement due to the dynamic action of the vehicle in response to road irregularities, are also calculated. A discussion of the conclusions drawn from the analysis is given.

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This dissertation presents an approach to reconstruct degenerate near-planar road surface in three-dimensional (3D) while automatically detect road defects. Three techniques are developed in this dissertation to establish the proposed approach. The first technique is proposed to reconstruct the degenerate near-planar road surface into 3D from one camera. Unlike the traditional Structure from Motion (SfM) technique which has the degeneracy issue for near-planar object 3D reconstruction, the uniqueness of the proposed technique lies in the use of near-planar characteristics of surfaces in the 3D reconstruction process, which solves the degenerate road surface reconstruction problem using only two images. Following the accuracy-enhanced 3D reconstructed road surface, the second technique automatically detects and estimates road surface defects. As the 3D surface is inversely solved from 2D road images, the detection is achieved by jointly identifying irregularities from the 3D road surfaces and the corresponding image information, while clustering road defects and obstacles using a mean-shift algorithm with flat kernel to estimate the depth, size, and location of the defects. To enhance the physics-driven automatic detection reliability, the third technique proposes and incorporates a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from supervised learning approaches which need labeled training images, the road anomaly detection network is trained by road surface images that are automatically labeled based on the reconstructed 3D surface information. In order to collect clear road surface images on the public road, a road surface monitoring system is designed and integrated for the road surface image capturing and visualization. The proposed approach is evaluated in both simulated environment and through real-world experiments. The parametric study of the proposed approach shows the small error of the 3D road surface reconstruction influenced by different variables such as the image noise, camera orientation, and the vertical movement of the camera in a controlled simulation environment. The comparison with traditional SfM technique and the numerical results of the proposed reconstruction using real-world road surface images then indicate that the proposed approach effectively reconstructs high quality near-planar road surface while automatically detects road defects with high precision, accuracy, and recall rates without the degenerate issue. / Doctor of Philosophy / Road is one of the key infrastructures for ground transportation. A good road surface condition can benefit mainly on three aspects: 1. Avoiding the potential traffic accident caused by road surface defects, such as potholes. 2. Reducing the damage to the vehicle initiated by the bad road surface condition. 3. Improving the driving and riding comfort on a healthy road surface. With all the benefits mentioned above, it is important to examine and check the road surface quality frequently and efficiently to make sure that the road surface is in a healthy condition. In order to detect any road surface defects on public road in time, this dissertation proposes three techniques to tackle the road surface defects detection problem: First, a near-planar road surface three-dimensional (3D) reconstruction technique is proposed. Unlike traditional 3D reconstruction technique, the proposed technique solves the degenerate issue for road surface 3D reconstruction from two images. The degenerate issue appears when the object reconstructed has near-planar surfaces. Second, after getting the accuracy-enhanced 3D road surface reconstruction, this dissertation proposes an automatic defects detection technique using both the 3D reconstructed road surface and the road surface image information. Although physics-based detection using 3D reconstruction and 2D images are reliable and explainable, it needs more time to process these data. To speed up the road surface defects detection task, the third contribution is a technique that proposes a self-supervised learning structure with data-driven Convolutional Neural Networks (CNN). Different from traditional neural network-based detection techniques, the proposed combines the 3D road information with the CNN output to jointly determine the road surface defects region. All the proposed techniques are evaluated using both the simulation and real-world experiments. Results show the efficacy and efficiency of the proposed techniques in this dissertation.