<p>The goal was to extract knowledge from data that is logged by the electronic system of</p><p>every Volvo truck. This allowed the evaluation of large populations of trucks without requiring additional measuring devices and facilities.</p><p>An evaluation cycle, similar to the knowledge discovery from databases model, was</p><p>developed and applied to extract knowledge from data. The focus was on extracting</p><p>information in the logged data that is related to the class labels of different populations,</p><p>but also supported knowledge extraction inherent from the given classes. The methods</p><p>used come from the field of unsupervised learning, a sub-field of machine learning and</p><p>include the methods self-organizing maps, multi-dimensional scaling and fuzzy c-means</p><p>clustering.</p><p>The developed evaluation cycle was exemplied by the evaluation of three data-sets.</p><p>Two data-sets were arranged from populations of trucks differing by their operating</p><p>environment regarding road condition or gross combination weight. The results showed</p><p>that there is relevant information in the logged data that describes these differences</p><p>in the operating environment. A third data-set consisted of populations with different</p><p>engine configurations, causing the two groups of trucks being unequally powerful.</p><p>Using the knowledge extracted in this task, engines that were sold in one of the two</p><p>configurations and were modified later, could be detected.</p><p>Information in the logged data that describes the vehicle's operating environment,</p><p>allows to detect trucks that are operated differently of their intended use. Initial experiments</p><p>to find such vehicles were conducted and recommendations for an automated</p><p>application were given.</p>

The goal was to extract knowledge from data that is logged by the electronic system of every Volvo truck. This allowed the evaluation of large populations of trucks without requiring additional measuring devices and facilities. An evaluation cycle, similar to the knowledge discovery from databases model, was developed and applied to extract knowledge from data. The focus was on extracting information in the logged data that is related to the class labels of different populations, but also supported knowledge extraction inherent from the given classes. The methods used come from the field of unsupervised learning, a sub-field of machine learning and include the methods self-organizing maps, multi-dimensional scaling and fuzzy c-means clustering. The developed evaluation cycle was exemplied by the evaluation of three data-sets. Two data-sets were arranged from populations of trucks differing by their operating environment regarding road condition or gross combination weight. The results showed that there is relevant information in the logged data that describes these differences in the operating environment. A third data-set consisted of populations with different engine configurations, causing the two groups of trucks being unequally powerful. Using the knowledge extracted in this task, engines that were sold in one of the two configurations and were modified later, could be detected. Information in the logged data that describes the vehicle's operating environment, allows to detect trucks that are operated differently of their intended use. Initial experiments to find such vehicles were conducted and recommendations for an automated application were given.

The Swedish road network is maintained by the Swedish Transport Administration, municipalities, and entrepreneurs with the goal of keeping the roads in satisfactory condition for traffic. The road operators are responsible for different roads and have several legislations that regulate construction and operation. One important aspect of winter road maintenance is the monitoring of the road situation ahead in order to call out resources for preventive measures. This study is performed at the company NIRA Dynamics with the purpose of going towards more digitized winter road information. The study explores different winter maintenance organizations in Sweden, investigates the importance of the information needed to be able to detect when roads are deemed too risky, and tries to gain an understanding of how the vehicle data provided by NIRA Dynamics best can provide a service for the winter road maintainers. This study is based on eight semi-structured interviews, user-tests aswell as a literature study. The findings of the study show that different winter maintenance organizations can differ a lot depending on the size and governing policies of the municipalities or entrepreneurs. The main differences can be found in their requirements and their method of monitoring the road situation ahead. The findings also show that the vehicle data is promising and has the potential to optimize and improve the overall winter maintenance planning. However, implementing and understanding the vehicle data in a real-world context requires collaboration from the different organizations to fulfill its value.

abstract: Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance. / Dissertation/Thesis / Doctoral Dissertation Applied Psychology 2017

Video image detection system (VDS) equipment provides real-time traffic data for monitored highways directly to the traffic management center (TMC) of the Georgia Department of Transportation. However, at any given time, approximately 30 to 35% of the 1,600 camera stations (STNs) fail to work properly. The main reasons for malfunctions in the VDS system include long term road construction activity and operational limitations. Thus, providing alternative data sources for offline VDS stations and developing tools that can help detect problems with VDS stations can facilitate the successful operation of the TMC. To estimate the travel speed of non-working STNs, this research examined global positioning system (GPS) data from vehicles using the ATMS-monitored freeway system as a potential alternative measure to VDS. The goal of this study is to compare VDS speed data for the estimation of the travel speed on freeways with GPS-equipped vehicle trip data, and to assess the differences between these measurements as a potential function of traffic and roadway conditions, environmental, conditions, and driver/vehicle characteristics. The difference between GPS and VDS speeds is affected by various factors such as congestion level (expressed as level of service), onroad truck percentage, facility design (number of lanes and freeway sub-type), posted speed limit, weather, daylight, and time of day. The relationship between monitored speed difference and congestion level was particularly large and was observed to interact with most other factors. Classification and regression tree (CART) analysis results indicated that driver age was the most relevant variable in explaining variation for the southbound of freeway dataset and freeway sub-type, speed limit, driver age, and number of lane were the most influential variables for the northbound of freeway dataset. The combination of several variables had significant contribution in the reduction of the deviation for both the northbound and the southbound dataset. Although this study identifies potential relationships between speed difference and various factors, the results of the CART analysis should be considered with the driver sample size to yield statistically significant results. Expanded sampling with larger number of drivers would enrich this study results.

Automotive diagnosis data are useful for the automotive OEMs and third parties in order to analyze the vehicle performance and driving behavior. This data can be access and read in real time situation using the On-Board Diagnosis system which is located inside the vehicle and is accessible by its socket. However, as the diagnosis data are in real time and not being stored, analyzing them is not easy. Therefore, in this project, a program on Raspberry Pi will be developed which is going to read diagnosis data and store them in a cloud database. The cloud database will handle the data storage and keep the data for further analysis and evaluations. The database will be accessible everywhere as it is using cloud technology. On the other hand, in order to provide easy and meaningful access to the data, a web application is developed in order to visualize the data by means of Graphs, Texts, and maps.

Time series as data in the machine learning research area has been dominated by prediction and forecasting techniques. Ever since the inception of generative models, the interest in generating time series has increased. Time series data appears in many different fields with financial and medical gathering much of the interest. This thesis is instead focusing on the automotive field with a heavy focus on wheel speed data. The issue with wheel speed data, or any other vehicle signal, is that they take a long time to gather since a person has to drive around in order to get the data.  This thesis investigates the possibility to generate vehicle signals with a large focus on wheel speed signals. To better understand the difference between different car models and which vehicle signals are most useful. The classification of vehicle time series was done with a stacked LSTM network. A thorough analysis of the network parameters was made and an accuracy of over 80\% was achieved when classifying 6 different vehicle models. For time series generation a GAN with LSTM networks was used, based on CRNNGAN. The generated samples were evaluated by people experienced with the data and by using both PCA and t-SNE. The result is bad and is too noisy. Only one of the vehicle signals could be generated in a satisfying manner and that signal was significantly less complex since it was a binary signal being either 0 or 1.

The large potential of big data and how it has brought value into various industries have been established in research. Since big data has such large potential if handled and analyzed in the right way, revealing information to support decision making in an organization, this thesis is conducted as a case study at an automotive manufacturer with access to large amounts of customer usage data of their vehicles. The reason for performing an analysis of this kind of data is based on the cornerstones of Total Quality Management with the end objective of increasing customer satisfaction of the concerned products or services. The case study includes a data analysis exploring how and if patterns about what affects fuel consumption can be revealed from aggregated customer usage data of trucks linked to truck applications. Based on the case study, conclusions are drawn about how a company can use this type of analysis as well as how to handle the data in order to turn it into business value. The data analysis reveals properties describing truck usage using Factor Analysis and Principal Component Analysis. Especially one property is concluded to be important as it appears in the result of both techniques. Based on these properties the trucks are clustered using k-means and Hierarchical Clustering which shows groups of trucks where the importance of the properties varies. Due to the homogeneity and complexity of the chosen data, the clusters of trucks cannot be linked to truck applications. This would require data that is more easily interpretable. Finally, the importance for fuel consumption in the clusters is explored using model estimation. A comparison of Principal Component Regression (PCR) and the two regularization techniques Lasso and Elastic Net is made. PCR results in poor models difficult to evaluate. The two regularization techniques however outperform PCR, both giving a higher and very similar explained variance. The three techniques do not show obvious similarities in the models and no conclusions can therefore be drawn concerning what is important for fuel consumption. During the data analysis many problems with the data are discovered, which are linked to managerial and technical issues of big data. This leads to for example that some of the parameters interesting for the analysis cannot be used and this is likely to have an impact on the inability to get unanimous results in the model estimations. It is also concluded that the data was not originally intended for this type of analysis of large populations, but rather for testing and engineering purposes. Nevertheless, this type of data still contains valuable information and can be used if managed in the right way. From the case study it can be concluded that in order to use the data for more advanced analysis a big-data plan is needed at a strategic level in the organization. The plan summarizes the suggested solution for the managerial issues of the big data for the organization. This plan describes how to handle the data, how the analytic models revealing the information should be designed and the tools and organizational capabilities needed to support the people using the information.