The increasing complexity of automated and AI-based vehicle systems necessitates innovative testing methodologies to ensure safety, reliability, and performance in diverse and dynamic scenarios. This paper introduces AV Test Operating System (ATOS), an open-source platform that seamlessly integrates simulated and physical testing environments for Advanced Driver-Assistance Systems and Automated Driving systems. ATOS supports comprehensive scenario evaluations under varied conditions such as weather, traffic, and connectivity environments by automating and orchestrating tests involving multiple simultaneous virtual and physical objects. Key contributions of this work include: 1) The introduction and design of ATOS, an open-source platform for automating and orchestrating complex vehicle testing scenarios. 2) A method for evaluating the repeatability of automated test orchestration systems. 3) Data analysis of two scenarios where ATOS was used in real-life AV testing. Results demonstrate ATOS’s effectiveness in executing repeatable and reliable tests across diverse configurations, highlighting its utility for research institutions, vehicle manufacturers, and testing facilities. Using ATOS in repeated runs, the time delay when triggering dynamic events was within 1 millisecond with virtual objects and 7 milliseconds when using physical objects. The positional variation between runs using virtual and real-life objects amounted to 6 and 20 cm, respectively. Future enhancements will focus on integrating real-world data and expanding ATOS’s capabilities to support evolving vehicle testing needs, contributing to safer, more robust transportation systems.

The safe and efficient operation of automated vehicles requires processing massive amounts of sensor data. However, the computational capabilities of vehicles are often limited. Recent results point to computational offloading as a promising solution for transferring raw sensor data to be processed elsewhere. This alleviates vehicles from performing costly computations while increasing their perception of complex environments. The work in this paper evaluates the resilience of such solutions, specifically focusing on adverse network conditions, which are often overlooked when evaluating computational offloading. To emulate adverse network conditions, we use synthetic network interference that includes, e.g., packet loss, throughput rate limiting, packet corruption, and RF attenuation. We conducted experiments with a real vehicle on a test track, where object detection was offloaded to an edge server. An optical camera, one of the most common perception sensors, was mounted on the vehicle to scan the environment. The experimental results indicate that network conditions can significantly impact the object detection performance. Packet loss and packet corruption proved to be especially impactful on the accuracy of detections. During the scenario of 5% packet corruption, the median value of false detections reached as high as 20%. The results emphasize the need for resilience and robustness to poor network conditions when designing computational offloading strategies.

 As automated vehicle technology advances, measures for their safe containment become increasingly important. To this end, geofencing is a prominent alternative as a fundamental technique for triggering specific actions when vehicles enter or leave a predefined operational area. Today’s geofencing methods usually fall short in safety-critical use cases, failing to contain vehicles, or triggering needless intervening actions. This work presents the novel model predictive geofence, which predicts future transgressions based on vehicle dynamics-informed real-time decisions. We studied its performance compared to representative approaches, both physically at the AstaZero Proving Ground in Sweden and through numerical calculations. Our geofence utilised the operational area more effectively than current approaches. Furthermore, the model predictive geofence successfully contained the vehicle to the operational area in all experiments, preventing exit with a low amount of false stops. The model predictive geofence presents an applicable approach for quick decision-making regarding the containment of vehicles in operational areas. 

Engineering companies face the challenge of optimising margins within product platforms while balancing individual product optimisation and maximising platform commonality. Key obstacles include organisational silos, diverse design variables, design space allocation, and varying time perspectives. This paper proposes a value-based modelling methodology that integrates both internal and external variety within the manufacturer. Using an automotive Head-Up Display (HUD) case study, we demonstrate how to effectively utilise platform margins to maximise technological variety and minimise internal variety, thereby enhancing long-term system value. This approach helps design teams understand the implications of their decisions, optimise platform margins to meet evolving technological demands, reduce costs, and maximise value. Our findings advance the understanding of margin optimisation in product platforms and support informed decision-making for engineering companies facing conflicting objectives. 

 Automated vehicles and active safety functions based on sensor technology have been identified by the automotive industry as catalysts for improved safety, sustainability, accessibility, and efficiency. As technology advances, the applications of these systems are constantly expanding. Alongside these advancements, methods must be developed to evaluate and test AD and ADAS system performance and reliability in relevant and repeatable ways. This work outlines the main challenges in developing and evaluating a test method for generating road spray, a turbulent mix of fine water particles that reduce visibility caused by vehicles driving on wet surfaces. A hardware prototype and an appurtenant evaluation process were designed and produced to realize the test method. The evaluation process includes an automated software tool to quantify the prototype’s ability to degrade visibility and a method for automating sensor calibration for data collection at different locations and times. One of the key findings is the challenge of eliminating external disturbances in the test environment. Factors such as light and wind conditions significantly affect visibility through spray. The work concludes that controlling these factors is essential for achieving test repeatability. We successfully recreated road spray in a controlled environment, attenuating a sensor’s perceptive ability in steps of up to 80%, repeatedly within ±5-15%.