To embrace safety while bringing autonomous vehicles (AVs) to public roads, AV manufacturers need to validate and verify the functionality and reliability of the control software. Real-road testing is time-consuming, tedious, costly, and unsafe for validation. Hence, simulation testing has been playing an important role in the market as a viable solution. This paper presents an approach that exploits both methods to find edge case scenarios and evaluates the software reliability of an existing AV shuttle, iseAuto, currently operating at the Tallinn University of Technology campus. To show the method’s effectiveness, a range of scenarios are generated and simulated for avoidance maneuvers by means of a low-fidelity simulator. Then, the scenarios that are found to be jeopardizing the AV are filtered and simulated by a high-fidelity simulator with the AV control software in the loop. Finally, to investigate the methodology and simulation reliability, a real study case is proposed using the AV shuttle. Results of the study suggest that the proposed toolchain is capable of tuning simulation models for automated driving development as well as validating safe AV operations.

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.

The safety of autonomous vehicles depends not only on perception and planning, but also on the correctness of low-level electronic signals that connect controllers and actuators. Errors at this interface, caused by hardware degradation, timing violations, software faults, or unexpected interactions, can lead to unsafe behavior even when high-level autonomy functions operate correctly. Existing safety mechanisms primarily focus on system behavior, trajectories, or controller design, leaving actuator-bound command streams largely unmonitored. This paper proposes a low-level, AI-enabled anomaly-detection layer for autonomous vehicle architectures. The core idea is to embed a lightweight observer within a virtualized master controller to monitor control-signal streams in real time without interfering with the primary control logic. The proposed framework combines a stacked LSTM sequence classifier with rule-based safety constraints and context-aware monitoring to detect physically implausible or temporally inconsistent command behavior before actuation. A proof-of-concept simulation study was conducted to evaluate the practicality of the approach using overtaking scenarios in a co-simulated high-level and low-level environment. The results show that the proposed concept can identify severe abnormal low-level behavior and provide preliminary warning/error indications, supporting its potential as a complementary safety layer at the control-to-actuation interface.

Digital Artificial Intelligence (AI), exemplified by Large Language Models (LLMs) such as ChatGPT, has achieved remarkable progress across a wide range of applications, driven not only by advances in algorithms but also by the emergence of a shared research ecosystem built upon commodity computing platforms, standardized software frameworks, open-source models, benchmark datasets, cloud infrastructure, and broadly accessible educational resources. In contrast, Autonomous Vehicles (AV), AI systems that perceive, reason, and act in the physical world, have advanced more slowly despite substantial public and private investment. Progress remains constrained by fragmented research and educational infrastructure that limits reproducibility, interoperability, scalable validation, and workforce development. This paper surveys the current state of the AV research ecosystem, including hardware platforms, autonomy software stacks, datasets, simulation environments, digital twins, testing and validation frameworks, and educational programs. Drawing lessons from the evolution of Digital AI, the paper identifies key gaps in accessibility, standardization, integration, and openness across the AV technology stack and outlines opportunities to develop shared research testbeds, modular open platforms, interoperable software and data ecosystems, common benchmarks, and interdisciplinary educational programs that can accelerate autonomous vehicle innovation. Finally, the paper provides a framework for evaluating AV research and educational infrastructure which identifies priorities for future investment.