In this paper we present a methodology and a platform using Fault Injection (FI) and Property-Based Testing (PBT). PBT is a technique in which test cases are automatically generated from a specification of a system property. The generated test cases vary input stimuli as well as the sequence in which commands are executed. FI is used to accelerate the occurrences of faults in a system to exercise and evaluate fault handling mechanisms and e.g. calculate error detection coverage. By combining the two we have achieved a way of randomly injecting different faults at arbitrary moments in the execution sequence while checking whether certain properties still hold. We use the commercially available tool QuickCheck for generating the test cases and developed FaultCheck for FI. FaultCheck enables the user to utilize fault models, commonly used during FI, from PBT tools like QuickCheck. We demonstrate our method and tools on a simplified example of two Airbag systems that should meet safety requirements. We can easily find a safety violation in one of the examples, whereas by using the AUTOSAR E2E-library implementation, exhaustive testing cannot reveal any such safety violation. This demonstrates that our approach on testing can reveal certain safety violations in a cost-effective way. © 2014 ACM.

We present a quadcopter platform built with commodity hardware that is able to do localization in GNSS-denied areas and avoid collisions by using a novel easy-to-setup and inexpensive ultrasound-localization system. We address the challenge to accurately estimate the copter's position and not hit any obstacles, including other, moving, quadcopters. The quadcopters avoid collisions by placing contours that represent risk around static and dynamic objects and acting if the risk contours overlap with ones own comfort zone. Position and velocity information is communicated between the copters to make them aware of each other. The shape and size of the risk contours are continuously updated based on the relative speed and distance to the obstacles and the current estimated localization accuracy. Thus, the collision-avoidance system is autonomous and only interferes with human or machine control of the quadcopter if the situation is hazardous. In the development of this platform we used our own simulation system using fault-injection (sensor faults, communication faults) together with automatically-generated tests to identify problematic scenarios for which the localization and risk contour parameters had to be adjusted. In the end, we were able to run thousands of simulations without any collisions, giving us confidence that also many real quadcopters can manoeuvre collision free in space-constrained GNSS-denied areas. ©2015 IEEE.

In this work we use our testing platform based on FaultCheck and QuickCheck that we apply on a quadcopter simulator. We have used a hardware platform as the basis for the simulator and for deriving realistic fault models for our simulations. The quadcopters have a collision-avoidance mechanism that shall take over control when the situation becomes hazardous, steer away from the potential danger and then give control back to the pilot, thereby preventing collisions regardless of what the pilot does. We use our testing platform to randomly generate thousands of simulations with different input stimuli (using QuickCheck) for hundreds of quadcopters, while injecting faults simultaneously (using FaultCheck). This way, we can effectively adjust system parameters and enhance the collision-avoidance mechanism. © 2015 IEEE

Cyclists are not well protected in accidents with other road users, and there are few active safety systems available for bicycles. In this study we have evaluated the use of inexpensive Real-Time Kinematic Satellite Navigation (RTK-SN) receivers with multiple satellite constellations together with dead reckoning for accurate positioning of bicycles to enable active safety functions such as collision warnings. This is a continuation of previous work were we concluded that RTK-SN alone is not sufficient in moderately dense urban areas as buildings and other obstructions degrade the performance of RTK-SN significantly. In this work we have added odometry to the positioning system as well as extending RTK-SN with multiple satellite constellations to deal with situations where the view of the sky is poor and thus fewer satellites are in view. To verify the performance of the positioning system we have used Ultra-Wideband radios as an independent positioning system to compare against while testing during poor conditions for RTK-SN. We were able to verify that adding dead reckoning and multiple satellite constellations improves the performance significantly under poor conditions and makes the positioning system more useful for active safety systems. © 2018 IEEE

Accurate positioning is a requirement for many applications, including safety-critical autonomous vehicles. To reduce cost and at the same time improving accuracy for positioning of autonomous vehicles, new methods, tools and research platforms are needed. We have created a low-cost testbed consisting of electronics and software, that can be fitted on model vehicles allowing them to follow trajectories autonomously with a position accuracy of around 3 cm outdoors. The position of the vehicles is derived from sensor fusion between Real-Time Kinematic Satellite Navigation (RTK-SN), odometry and inertial measurement, and performs well within a 10 km radius from a base station. Trajectories to be followed can be edited with a custom GUI, where also several model vehicles can be controlled and visualized in real time. All software and Printed Circuit Boards (PCBs) for our testbed are available as open source to make customization and development possible. Our testbed can be used for research within autonomous driving, for carrying test equipment, and other applications where low cost and accurate positioning and navigation is required. © 2018 Benjamin Vedder et al.

Autonomous vehicles need accurate and dependable positioning, and these systems need to be tested extensively. We have evaluated positioning based on Ultra-Wide Band (UWB) ranging with our self-driving model car using a highly automated approach. Random drivable trajectories were generated, while the UWB position was compared against the Real-Time Kinematic Satellite Navigation (RTK-SN) positioning system that our model car also is equipped with. Fault injection was used to study the fault tolerance of the UWB positioning system. Addressed challenges are: automatically generating test cases for real-time hardware, restore the state between tests and to maintain safety by preventing collisions. We were able to automatically generate and carry out hundreds of experiments on the model car in real time, and re-run them consistently with and without fault injection enabled. Thereby we demonstrate one novel approach to perform automated testing on complex real-time hardware.