The road surface friction is an important characteristic that must be measured accurately to navigate vehicles effectively under different conditions. This parameter is very difficult to estimate correctly as it can take up a value from a broad spectrum of possibilities and the knowledge of this characteristic is of utmost significance in modern day automotive applications. The possible real-time knowledge of friction opens a new range of improvements to the active safety systems such as the Electronic Stability Control (ESC) and Anti-lock Braking Systems (ABS) in addition to providing computerized support to safety applications. The aim of the research is to take an engineering approach to the problem and design a simple and a robust algorithm that can be implemented in any automotive application of choice. After integrating the load transfer model with the four wheel vehicle model, the Dugoff tire models are combined with the aforementioned model to represent the plant model. Using the plant model to design an emulator, the sensor measurements are created and these measurements are then used by a non linear estimator such as the Unscented Kalman Filter to predict the forces at the tires. Friction is then calculated for every iteration and then passed back into the loop.In the end, a comparison of different design methodologies, implementation techniques and performance along with design decisions are discussed so that the current work can be implemented on a real-time controller. In addition to this, a section is dedicated towards highlighting the difference that prior friction information has on the stopping distance of a vehicle. For this purpose, a demonstration is made by creating an ABS control system that uses the predicted friction information and the performance improvement is documented. / Master of Science / The goal of the research is to identify methods in which the road surface friction can be detected by the on board computers present on modern day cars. Drivers have the ability to determine the grip on the road surface through various mechanisms, for instance if a driver sees a patch of ice on the road when driving, their normal response is to take the foot off the gas and drive without giving much steering input to avoid a slide. Another input that the driver can use to assess the grip is through the 'steering feel', which is the ability to differentiate different driving conditions through the force feedback from the steering wheel. There have been numerous approaches to help teach the computer to detect these road conditions so that it can operate other computerized systems such as the ABS(Anti-lock Braking System) and ESC( Electronic Stability Control) programs with better accuracy. This work is an attempt to contribute to this vital area of study. At the end of the study, an algorithm to predict the dynamic estimate of friction has been developed and the improvement in the performance of the Anti-lock braking system using this friction estimate has been demonstrated

In this thesis project, brake performance of heavy vehicles is improved by the development of new wheel-based functions for a longitudinal slip control braking system using novel Fast Acting Braking Valves (FABVs). To achieve this goal, Volvo Trucks' vehicle dynamics model has been extended to incorporate the FABV system. After validating the updated model with experimental data, a slip-slope based recursive least squares friction estimation algorithm has been implemented. Using information about the tire-road friction coefifcient, the sliding mode slip controller has been made adaptive to different road surfaces by implementing a friction dependent reference slip signal and switching gain for the sliding mode controller. This switching gain is further optimized by means of a novel on-line optimization algorithm. Simulations show that the on-line friction estimation converges close to the reference friction level within one second for hard braking. Furthermore, using this information for the optimized controller has resulted in reduction of braking distance on most road surfaces of up to 20 percent, as well as in most cases a reduction in air usage.

The contact between the tire and the road is the key enabler of vehicle acceleration, deceleration and steering. However, under the circumstances of sudden changes to the road conditions, the driver`s ability to maintain control of the vehicle maybe at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road contact parameters (ranging from kinematic conditions of the tire to its dynamic properties) are available. At the present stage of development, tire-road contact parameters are indirectly estimated using observers based on vehicle dynamics measurements (acceleration, yaw and roll rates, suspension deflections, etc). Although these methods present a relatively accurate solution, they rely heavily on tire and vehicle kinematic formulations and break down in case of abrupt changes in the measured quantities. To address this problem, researchers have been developing certain sensor based advanced tire concepts for direct measurement of the tire-road contact parameters. Thus the new terms "Intelligent Tire" and "Smart Tire", which mean online tire monitoring are thus enjoying increasing popularity among automotive manufacturers and formed the motivation for this thesis to explore the possibility of developing an intelligent tire system. The development of the so called "intelligent tire/ smart tire system" is expected to spur the development of a new generation of vehicle control system with modified control strategies, leveraging information directly coming from the interface between the tire and the road, and in turn significantly reducing the risk of accidents. The specific contributions of this thesis include the following: • Development of an intelligent tire system, with a special attention to development of measurement and sensor feature extraction methodologies of acceleration signals coming from sensors fixed to the tire innerliner • Design of an integrated vehicle state estimator for application to global chassis control • Development of a model-based tire-road friction estimation algorithm • Development of an intelligent tire based adaptive wheel slip controller for anti-lock brake system (ABS) • Development of a piezoelectric vibration energy harvesting system with an adaptive frequency tuning mechanism for intelligent tires / Master of Science

During recent years, great progress has been made within the field of deep learning, and more specifically, within neural networks. Deep convolutional neural networks (CNN) have been especially successful within image processing in tasks such as image classification and object detection. Car manufacturers, amongst other actors, are starting to realize the potential of deep learning and have begun applying it to autonomous driving. This is not a simple task, and many challenges still lie ahead. A sub-problem, that needs to be solved, is a way of automatically determining the road conditions, including the friction. Since many modern cars are equipped with cameras these days, it is only natural to approach this problem with CNNs. This is what has been done in this thesis. First, a data set is gathered which consists of 37,000 labeled road images that are taken through the front window of a car. Second, CNNs are trained on this data set to classify the friction of a given road. Gathering road images and labeling them with the correct friction is a time consuming and difficult process, and requires human supervision. For this reason, experiments are made on a second data set, which consist of 54,000 simulated images. These images are captured from the racing game World Rally Championship 7 and are used in addition to the real images, to investigate what can be gained from this. Experiments conducted during this thesis show that CNNs are a good approach for the problem of estimating the road friction. The limiting factor, however, is the data set. Not only does the data set need to be much bigger, but it also has to include a much wider variety of driving conditions. Friction is a complex property and depends on many variables, and CNNs are only effective on the type of data that they have been trained on. For these reasons, new data has to be gather by actively seeking different driving conditions in order for this approach to be deployable in practice. / Under de senaste åren har det gjorts stora framsteg inom maskininlärning, särskilt gällande neurala nätverk. Djupa neurala närverk med faltningslager, eller faltningsnätverk (eng. convolutional neural network) har framför allt varit framgångsrika inom bildbehandling i problem så som bildklassificering och objektdetektering. Biltillverkare, bland andra aktörer, har nu börjat att inse potentialen av maskininlärning och påbörjat dess tillämpning inom autonom körning. Detta är ingen enkel uppgift och många utmaningar finns fortfarande framöver. Ett delproblem som måste lösas är ett sätt att automatiskt avgöra väglaget, där friktionen ingår. Eftersom många nya bilar är utrustade med kameror är det naturligt att försöka tackla detta problem med faltningsnätverk, vilket är varför detta har gjorts under detta examensarbete. Först samlar vi in en datamängd beståendes av 37 000 bilder tagna på vägar genom framrutan av en bil. Dessa bilder kategoriseras efter friktionen på vägen. Sedan tränar vi faltningsnätverk på denna datamängd för att klassificera friktionen. Att samla in vägbilder och att kategorisera dessa är en tidskrävande och svår process och kräver mänsklig övervakning. Av denna anledning utförs experiment på en andra datamängd beståendes av 54 000 simulerade bilder. Dessa har blivit insamlade genom spelet World Rally Championship 7 där syftet är att undersöka om prestandan på nätverken kan ökas genom simulerat data och därmed minska kravet på storleken av den riktiga datamängden. De experiment som har utförts under examensarbetet visar på att faltningsnätverk är ett bra tillvägagångssätt för att skatta vägfriktionen. Den begränsande faktorn i det här fallet är datamängden. Datamängden behöver inte bara vara större, men den måste framför allt täcka in ett bredare urval av väglag och väderförhållanden. Friktion är en komplex egenskap och beror på många variabler, och faltningsnätverk är endast effektiva på den typen av data som de har tränats på. Av dessa anledningar behöver ny data samlas in genom att aktivt söka efter nya körförhållanden om detta tillvägagångssätt ska vara tillämpbart i praktiken.

The thesis work investigates brake friction estimation strategies. The friction between the brake disc and brake pads is not constant during the braking application and contributes to the amount of brake torque achieved at the wheels. In this study, it is considered that any change in the brake torque between the requested and achieved values is only due to the varying brake friction coefficient. The work gives three different approaches to estimate the brake friction coefficient using two prominent state estimation strategies, Unscented Kalman Filter and Moving Horizon Estimation. The inputs to the estimators are obtained from a Vehicle model, which is built using the wheel balance equations. The estimators have been tuned to minimize the estimation error in nominal conditions and tested for their robustness through a wide analysis, where the sensitivity of the strategies is checked against a spectra of potential system parameters and boundary conditions. Throughout all the analysis, the developed models estimate the brake friction coefficient within an acceptable error range. This work opens up opportunities for further studies that can be performed using the built estimator models. / Detta examensarbete studerar strategier för skattning av bromsfriktion. Friktionen mellan bromsskivan och bromsbeläggen är inte konstant under bromsförloppet och det är denna som genererar bromsmomentet för varje hjul. I detta arbete så antas att förändringen i bromsmoment mellan begärd och uppnått endast är på grund av varierande bromsfriktion mellan bromsbelägg och bromsskiva. Arbetet presenterar tre olika sätt att skatta bromsfriktionen genom användning av två kända skattningsmetoder, Uncented Kalman Filter och Moving Horizon Estimation. Ingående värden till skattningsmetoderna fås från en fordonsmodell som är byggd med hjälp av hjulbalansekvationer. Skattningsmetoderna har justerats så att de minimerar skattningsfelet i nominella fall och de är testade för robusthet genom en bred analys där känsligheten hos metoderna testas genom en flora av potentiella systemparametrar och gränsvärden. Genom hela analysen så uppnår de utvecklade skattningsmetoderna bromsfriktionsvärden med acceptabla felnivåer. Detta arbete öppnar upp för möjligheter för vidare analyser där de utvecklade metoderna kan användas.