Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors’ operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.

Validation and Verification (V&V) of Artificial Intelligence (AI) based cyber physical systems such as Autonomous Vehicles (AVs) is currently a vexing and unsolved problem. AVs integrate subsystems in areas such as detection, sensor fusion, localization, perception, and path planning. Each of these subsystems contains significant AI content integrated with traditional hardware and software components. The complexity for validating even a subsystem is daunting and the task of validating the whole system is nearly impossible. Fundamental research in advancing the state-of-the-art for AV V&V is required. However, for V&V researchers, it is exceedingly difficult to make progress because of the massive infrastructure requirements to demonstrate the viability of any solution. This paper presents PolyVerif, the world’s first open-source solution focused on V&V researchers with the objective of accelerating the state-of-the-art for AV V&V research. PolyVerif provides an AI design and verification framework consisting of a digital twin creation process, an open-source AV engine, access to several open-source physics based simulators, and open-source symbolic test generation engines. PolyVerif’s objective is to arm V&V researchers with a framework which extends the state-of-the-art on any one of the many major axes of interest and use the remainder of the infrastructure to quickly demonstrate the viability of their solution. Given its open-source nature, researchers can also contribute their innovations to the project. Using this critical property of open-source environments, the innovation rate of the whole research community to solve these vexing issues can be greatly accelerated. Finally, the paper also presents results from several projects which have used PolyVerif.

In the field of education, the recent revolution in the large language model (LLM) space has enabled a whole host of interesting applications, such as content generation, support, and even personalized learning. While there are many ad-hoc experiments in flight, scientific studies on the effectiveness of these techniques have been limited. In order to increase the scientific rigor and potential for experimental reproducibility, the Tallinn University of Technology (TalTech) team deployed an artificial intelligence (AI) chatbot within the context of a traditional mainstream mechanics physics course and instrumented the class to facilitate a scientific study on utility. The AI chatbot focused on course support and tutoring in the Estonian language, and the scientific design-for-experiment focused on impact for students, instructors, and course designers. The study revealed measurable gains in instructor productivity and student access. The study also demonstrated the expected need for additional due diligence required to manage AI hallucinations. Perhaps most interestingly, the study revealed the unexpected benefits of cataloguing student chat interactions as a rich data source for the development of instructional materials and future course design. In fact, LLMs were also very useful to evaluate these AI chatbot conversations. Overall, this scientific study provides insights for the educational community into the leverage of using AI chatbots for instruction and in dramatically increasing access by enabling the use of a local language.

The open-source paradigm is well known for its unique potential to crowd-source innovation with spectacular examples of success, such as Linux. The knowledge base of methods to build more successful open-source communities has been growing with concepts such as independent governance, community building, and funding mechanisms. In mobility, an open-source autonomy stack called Autoware was the basis of a significant public transportation research project called ISEAUTO, and a companion project, PolyVerif, focused on building validation and verification technologies. This paper outlines the real-life experiences of two teams using the Autoware framework, providing lessons and recommendations for improving open-source development.