The development of Automated Driving Systems (ADS) has been ongoing for decades in varying levels of sophistication. Levels of automation are defined by Society of American Engineers (SAE) as 0 through 5, with 0 being full human control and 5 being full automation control. Another way to describe levels of automation is through concepts of Functional Safety (FuSa) and Operational Safety (OpSa). These terms of FuSa and OpSa are important, because ADS testing relies on both. Current recommendations for ADS testing include both OpSa and FuSa requirements. However, an examination of ADS safety requirements (e.g., industry reports, post-crash analysis reports, etc.) reveals that ADS safety arguments, in practice, depend almost completely on well-trained human operators, referred to in the industry as in vehicle fallback test drivers (IFTD). To date, the industry has never fielded a truly SAE L4 ADS on public roads due to this persistent hurdle of needing a human operator for Operational Safety. There is a tendency in ADS testing to reference International Standards Organization (ISOs) for validated vehicles for vehicles that are still in development (i.e., unvalidated). To be clear, ISOs for ADS end products are not necessarily applicable to ADS in development. With this in mind, there is a clear gap in the industry for unvalidated ADS literature. Because of this gap, ADS testing for unvalidated vehicles often relies on safety requirements for validated vehicles. This issue remains a significant challenge for ADS testing. Recognizing this gap in on-road, in-development vehicle safety, there is a need for the ADS industry to develop a clear strategy for transitioning from an IFTD (Operational Safety) to an ADS (Functional Safety). Therefore, the purpose of this thesis is to present a framework for transitioning from Operational Safety to Functional Safety. The framework makes this possible through an inductive analysis of available definitions of onroad safety to arrive at a definition that leverages Functional and Operational Safety along a continuum. Ultimately, the framework aims to contribute to onroad safety testing for the ADS industry. / Master of Science / The development of Self-Driving Cars has been ongoing for decades in varying levels of sophistication. Levels of automation are defined by Society of American Engineers (SAE) as 0 through 5, with 0 being full human control and 5 being full automation control. Another way to describe levels of automation is through concepts of Robotic Control and Human Control. If a vehicle relies completely on Human Control, a human operator is responsible for all on-road safety. On the other hand, a fully autonomous would be considered fully in Robotic Control. These terms of Robotic Control and Human Control are important, because Self-Driving Car testing relies on both. Current recommendations for Self-Driving Car testing include both Robotic Control and Human Control requirements. However, an examination of Self-Driving Cars documentation (e.g., industry reports, post-crash analysis reports, etc.) reveals that Self-Driving Car safety arguments, in practice, depend almost completely on well-trained human operators. To date, the industry has never fielded a truly SAE L4 Self-Driving Car on public roads due to this persistent hurdle of needing a human operator for Human Control. There is a tendency in Self-Driving Car testing to reference standars for validated vehicles for vehicles that are still in development (i.e., unvalidated). To be clear, standards for Self-Driving Car end products are not necessarily applicable to Self-Driving Cars in development. With this in mind, there is a clear gap in the industry for unvalidated Self-Driving Car literature. Because of this gap, Self-Driving Car testing for unvalidated vehicles often relies on documentation for validated vehicles. This issue remains a significant challenge for Self-Driving Car testing. Recognizing this gap in on-road, in-development vehicle safety, there is a need for the Self-Driving industry to develop a clear strategy for transitioning from Human Control to Robot Control. Therefore, the purpose of this thesis is to present a framework for transitioning from Human to Robot Control. The framework makes this possible through an inductive analysis of available definitions of onroad safety to arrive at a definition that leverages all definitions of Safety along a continuum. Ultimately, the framework aims to contribute to onroad safety testing for the Self-Driving industry.

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.

Several standardized courses for Formula SAE (FSAE) testing are introduced and described with sufficient detail to be reproduced by any Formula SAE team. Basic analysis methods for the courses are given as well as explanations of how those analyses could be used. On-car data from the Global Formula Racing (GFR) SAE cars is used to verify the analysis methods, give estimates to unknown variables, and show the relevance of the standard testing courses. Using the courses and methods described in this paper should allow standardized comparison of FSAE car performance, as well as provide a method to verify simulations and evaluate changes in vehicle performance from tuning. Instrumentation of all suspension member forces with strain gauge load cells is shown to be an extremely powerful tool for measuring vehicle performance and quantifying vehicle dynamic characteristics. The design and implementation of strain gauge load cells is described in detail to provide a template for reproducing similar results in other vehicles. Data from the GFR 2011 FSAE car is used throughout the paper to: show the design process for making effective suspension member load cells, show the calibration processes necessary to ensure quality data is collected, illustrate the calculation of suspension corner forces, and show the effectiveness of measuring vehicle dynamic characteristics with this technique. Using the methods described in this paper should provide data that allows a more complete and thorough understanding of on-car vehicle dynamics. This data may be used to validate vehicle models. / Graduation date: 2012

Passenger safety and comfort are important aspects in the process of vehicle development. The world is heading towards developing the safest possible vehicle on the road. Using vehicle motion control functions is one of the ways to enhance vehicle stability. These motion control functions need to be developed in an energy optimised way. By complementing some of the development process with virtual models, both the development time and cost could be minimised. Hence, a sustainable way of control function development could be achieved. In order to verify, validate and calibrate vehicle motion control functions, an accurate model of the virtual vehicle is required. Hence, a research question on how good the virtual model needs to be for the purpose has been addressed. This report suggests a framework in order to determine the capabilities of a virtual vehicle.In this report, a comparison study has been carried out by exciting the real car and virtual model of a Volvo XC90 with a focus of covering the six degrees of freedom (Yaw, pitch, roll, longitudinal, lateral and vertical). A semi automated framework that possesses the capability of automating the testing in a virtual platform has been established. From the test results, the virtual vehicle capabilities were determined. Further, in the second part of the report, an example use case has been considered by taking two calibration sets of Electronic stability control (ESC) system in order to verify the previously established framework.The analysis includes various levels of plant and controller complexity such as Model-in-loop, Software-in-loop and Hardware-in-loop and on two different road surfaces, low friction and high friction. From the observations, the virtual models considered correlates well for the purpose of verification and validation. However, for the purpose of calibration, the models need to be fine-tuned in the virtual platform. Furthermore, the correlation on low friction road surface could be improved by simulating the tests using an advanced tyre model. Overall, this study helps in choosing the correct complexity of various subsystems in a vehicle for the purpose of verification, validation and calibration of vehicle motion control functions. / Passagerarsäkerhet och komfort är viktiga aspekter i utvecklingen av ett fordon. Världen är på väg mot att utveckla säkraste möjliga fordon på vägen. Användning av fordonetse rörelsekontrollfunktioner är ett av sätten att förbättra fordonets stabilitet. Dessa rörelsekontrollfunktioner måste utvecklas på ett energioptimerat sätt. Genom att komplettera en del av utvecklingsprocessen med virtuella modeller kan både utvecklingstid och kostnad minimeras. Därför kan ett hållbart sätt att utveckla funktionerna för kontrollfunktioner uppnås. För att verifiera, validera och kalibrera fordonets rörelsekontrollfunktioner krävs en detaljerad modell av ett virtuellt fordon. Därför har en forskningsfråga om hur bra den virtuella modellen måste vara för ändamålet behandlats. Denna rapport föreslår ett ramverk för att bestämma funktionerna hos virtuella fordon.I denna rapport har en jämförelsestudie genomförts genom att excitera den verkliga bilen och den virtuella modellen av en Volvo XC90 med fokus på att täcka de sex frihetsgraderna (gir, nick, roll, längs, lateral, vertikal). Ett semi-automatiserat ramverk som har förmågan att automatisera testningen i en virtuell plattform har skapats. Från testresultaten bestämdes de virtuella fordonsfunktionerna. Vidare har i den andra delen av rapporten ett exempel på användningsfall beaktats genom att man tar två kalibreringsuppsättningar av ESC-system (Electronic Stability Control) för att verifiera det tidigare etablerade ramverket.Analysen innefattar olika nivåer av modell- och styrenhetskomplexitet såsom Model-in-loop, Software-in-loop och Hardware-in-loop och på två olika vägytor, låg friktion och hög friktion. Enligt observationerna är de virtuella modellerna väl korrelerade för verifiering och validering. För kalibreringen måste dock modellerna finjusteras på den virtuella plattformen. Dessutom kunde korrelationen på lågfriktionsvägytan förbättras genom att simulera testerna med hjälp av en avancerad däckmodell. Sammantaget hjälper den här studien att välja rätt komplexitet hos olika delsystem i ett fordon för verifiering, validering och kalibrering av fordonets rörelsekontrollfunktioner.