A car with underinflated tires can lead to both safety and environmental issues. To combat this, markets have begun requiring new cars to feature a Tire Pressure Monitoring System. Systems without pressure sensors are referred to as indirect Tire Pressure Monitoring Systems, often utilizing wheel speed sensors in combination with other available sensor information to detect tire pressure losses.  NIRA Dynamics is a company founded in Linköping, Sweden, most known for its indirect Tire Pressure Monitoring System called TPI. TPI needs to be verified in a large number of scenarios, which may be both difficult and expensive to realize in real vehicle tests. The purpose of this master thesis was to investigate and model what physical phenomena are associated with having a trailer connected to a car, relevant for TPI. The goal was to construct a hybrid simulation framework, making it possible to modify car-only data to reflect the effects of having a trailer connected.  A car-trailer model was developed, showing close resemblance in simulations to real collected car-trailer sensor data. The model was then used to design a hybrid simulation framework, where car-only sensor signals were modified to mimic having different types of trailers attached. The hybrid simulation results show close resemblance to real collected trailer sensor data. By not requiring real trailer data for every scenario to evaluate software performance on, the proposed framework opens up the possibility to simulate data from a much larger number of trailer combinations than would otherwise have been feasible to test in real vehicle tests.

In the field of mobile robotics, many applications require feedback control laws that provide perfect path following. Previous work has shown that transverse feedback linearization is an effective approach to designing path following controllers that achieve perfect path following and path invariance. This thesis uses transverse feedback linearization and augments it with dynamic extension to present a framework for designing path following controllers for certain kinematic models of mobile robots. This approach can be used to design path following controllers for a large class of paths. While transverse feedback linearization makes the desired path attractive and invariant, dynamic extension allows the closed-loop system to achieve the desired motion along the path. In particular, dynamic extension can be used to make the mobile robot track a desired velocity or acceleration profile while moving along a path.

In the field of mobile robotics, many applications require feedback control laws that provide perfect path following. Previous work has shown that transverse feedback linearization is an effective approach to designing path following controllers that achieve perfect path following and path invariance. This thesis uses transverse feedback linearization and augments it with dynamic extension to present a framework for designing path following controllers for certain kinematic models of mobile robots. This approach can be used to design path following controllers for a large class of paths. While transverse feedback linearization makes the desired path attractive and invariant, dynamic extension allows the closed-loop system to achieve the desired motion along the path. In particular, dynamic extension can be used to make the mobile robot track a desired velocity or acceleration profile while moving along a path.

This thesis investigates and implements the sensor fusion necessary to autonomously reverse a full size truck and trailer system. This is done using a LiDAR mounted on the rear of the truck along with a RTK-GPS. It is shown that the relative angles between truck-dolly and dolly-trailer can be estimated, along with global position and global heading of the trailer. This is then implemented in one of Scania's test vehicles, giving it the ability to continuously estimate these states. A controller is then implemented, showing that the full scale system can be stabilised in reverse motion. The controller is tested both on a static reference path and a reference path received from a motion planner. In these tests, the controller is able to stabilise the system well, allowing the truck to do complex manoeuvres backwards. A small lateral tracking error is present, which needs to be further investigated.