Metaverse for Safer Roadways: An Immersive Digital Twin Framework for Exploring Human-Autonomy Coexistence in Urban Transportation Systems

Societal-scale deployment of autonomous vehicles requires them to coexist with human drivers, necessitating mutual understanding and coordination among these entities. However, purely real-world or simulation-based experiments cannot be employed to explore such complex interactions due to safety and reliability concerns, respectively. Consequently, this work presents an immersive digital twin framework to explore and experiment with the interaction dynamics between autonomous and non-autonomous traffic participants. Particularly, we employ a mixed-reality human-machine interface to allow human drivers and autonomous agents to observe and interact with each other for testing edge-case scenarios while ensuring safety at all times. To validate the versatility of the proposed framework's modular architecture, we first present a discussion on a set of user experience experiments encompassing 4 different levels of immersion with 4 distinct user interfaces. We then present a case study of uncontrolled intersection traversal to demonstrate the efficacy of the proposed framework in validating the interactions of a primary human-driven, autonomous, and connected autonomous vehicle with a secondary semi-autonomous vehicle. The proposed framework has been openly released to guide the future of autonomy-oriented digital twins and research on human-autonomy coexistence.

Towards Validation of Autonomous Vehicles Across Scales using an Integrated Digital Twin Framework

Autonomous vehicle platforms of varying spatial scales are employed within the research and development spectrum based on space, safety and monetary constraints. However, deploying and validating autonomy algorithms across varying operational scales presents challenges due to scale-specific dynamics, sensor integration complexities, computational constraints, regulatory considerations, environmental variability, interaction with other traffic participants and scalability concerns. In such a milieu, this work focuses on developing a unified framework for modeling and simulating digital twins of autonomous vehicle platforms across different scales and operational design domains (ODDs) to help support the streamlined development and validation of autonomy software stacks. Particularly, this work discusses the development of digital twin representations of 4 autonomous ground vehicles, which span across 3 different scales and target 3 distinct ODDs. We study the adoption of these autonomy-oriented digital twins to deploy a common autonomy software stack with an aim of end-to-end map-based navigation to achieve the ODD-specific objective(s) for each vehicle. Finally, we also discuss the flexibility of the proposed framework to support virtual, hybrid as well as physical testing with seamless sim2real transfer.

Nigel -- Mechatronic Design and Robust Sim2Real Control of an Over-Actuated Autonomous Vehicle

Simulation to reality (sim2real) transfer from a dynamics and controls perspective usually involves re-tuning or adapting the designed algorithms to suit real-world operating conditions, which often violates the performance guarantees established originally. This work presents a generalizable framework for achieving reliable sim2real transfer of autonomy-oriented control systems using multi-model multi-objective robust optimal control synthesis, which lends well to uncertainty handling and disturbance rejection with theoretical guarantees. Particularly, this work is centered around an actuation-redundant scaled autonomous vehicle called Nigel, with independent all-wheel drive and independent all-wheel steering architecture, whose enhanced configuration space bodes well for robust control applications. To this end, we present a systematic study on the complete mechatronic design, dynamics modeling, parameter identification, and robust stabilizing as well as steady-state tracking control of Nigel using the proposed framework, with experimental validation.