Transportation and Mobility Report-Out

1. Transportation and Mobility Report-Out Co-chairs: Robert Wagner (ORNL), Jibo Sanyal (ORNL), Stan Young (NREL)

2. Major Topics Discussed • Digital twin • Full observability - Real time, controls, disruptions, data gaps • State agencies, etc. • Multi-scale physics and reduced order models for vehicular systems • Combustion – AI for rare events • Multi-scale physics and AI to help • Reduced order models, chaotic & non-chaotic • In-vehicle AI • Perception, end-to-end driving, machine driving for humans • Vehicle to vehicle & vehicle to human behaviors & evaluate dynamics • Computation needs on board • Cross-domain smarter environment/cities • Optimization, electric grid, energy storage, transactive energy, resilience, infrastructure • Social & behavioral • Transportation to Mobility - Lyft/Uber, travel demands, access to goods and services

3. High Fidelity Digital Twins for Mobility Systems • Problem: Transportation systems in our cities are complex, not well understood. • Potential Impact: A virtual laboratory for observable transportation systems that allows municipalities and regions to develop real-time adaptive, optimized systems and evaluate policy changes, infrastructure investments, and impacts from future technologies. • Key Challenges: Significant data gaps exist, many stakeholders with heterogenous data sources. Insufficient resolution or completeness in data sources. Real-time data infrastructure and APIs to support real time controls do not exist. When data does begin to exist, it will come in a massive, streaming torrent. Accuracy and validation of models for ‘future’ scenarios. • Critical AI Investments: Data cleaning, imputing and interpolation. Event prediction, volume forecasting. Evaluation of design/parameter space - AI models and architectures. AI for high velocity, real-time applications on streaming data. Machine vision to support video analytics. Workflows for big datasets (city-scale data streams). Faster simulation through proxy models and AI surrogates – approximate solutions quickly. Modeling uncertainty. Transfer learning for reapplication/adaption.

4. Multi-scale physics-based reduced order models for vehicular systems • Current simulation and modeling approaches do not predict relevant experimental results, limiting ability to optimize powertrain design and control strategies for vehicle efficiency • Physics at some scales not accurately resolved • Fully resolved simulations will remain prohibitively expensive • AI will learn surrogate models from ensemble of engineering-scale simulations, targeted high-fidelity simulations, and experimental data • Higher fidelity simulations to enforce physical meaningfulness • Engineering simulations to explore design/optimization space • Experimental data provides ground truth • Multiple scales of, e.g. engine combustion phenomena: • Multi-phase fluid dynamics – flows, sprays; combustion chemistry/kinetics; heat transfer • Cyclic coupling of sequential events • AI resolves multiple scales through surrogate models and guides further experiments • Satisfy realizability, physical constraints, consistent linking of multi-fidelity data

5. Level 5 Vehicle Autonomy • Potential breakthrough including: • Test & Evaluation of CAV AI Systems • Real / Simulated Data Sets for AI Development • Multi-Modal Sensor Fusion for Perception • Infrastructure Augmentation • Lower Power / High Intelligence On-Board Compute for CAV • Current data sets are limited to specific real world scenarios • Performance challenges with current approaches: • Requires Exascale level compute for development, test, and evaluation of AI Level 5 Autonomy • What science grand challenge(s) can you enable via this breakthrough • Fully functional autonomous vehicles across environments • Societal Benefits: • Optimization of the Mobility System of Systems (local up to national) for specific purposes (e.g., safety, time) • Increase in personal mobility / accessibility across society • Reduction in fatalities, emissions per vehicle/mile, energy consumption

6. Cross-Domain Smarter Environments/Cities • Problems: Cities are a complex system of systems: each individual interaction is integrated into a larger understanding of the whole system (e.g., electric grid, mobility, buildings, etc.). Demand for resources is dynamic, depends on social aspects. • Potential Impact: A coupled system of systems with dynamic demand modeling will provide support for operation and planning at regional/any scale • Key Challenges: There are significant data gaps due to coverage and heterogeneity of data. Need to find a fine balance between individual privacy and getting enough data to be useful. Individual subsystems operate in silos – need to break silos to integrate into system of systems. Social acceptance by general public. • Critical AI Investments: Trust in AI, interpretability of results, approximate and fast co-optimization between different subsystems, data-driven models of individual physical systems.