1. Sensing, Collection, Mining of Vehicular Data Group – 1 Breakout Session DriveSense NSF Workshop Oct 31, 2014

2. Breakout Group I – Sensing, Collection, & Mining Infrastructure Sensing vs. Crowd Sensing Incentives – What – How to evaluate Unified Tools – Data Processing/Analysis – Validation Large-Scale Sensing Data! – How Large should be large? – How to execute? Privacy/Security – Do we need to redefine privacy? – Privacy vs. Safety? Faulty Sources vs. Malicious Behavior? New Mining tools vs. existing one? – Is there any unique characteristics that defines driving data? Heterogeneity – Data type sources vs. Data types Role of giant companies (e.g., Google, Navtaq)

3. What does the community lack? Libraries of Data, from various sources – Infrastructure-based – Crowd sourced – In-vehicle – Satellite, social networks data, etc. – For: Mobility, Energy, Pollution, Health, Livability, Safety, Activity, Context (weather, infra condition, etc.), etc. Common Tools – Middleware for apps, for crowd sensing, APIs, etc. – Mining, data fusion, scalable data processing, visualization – Algorithms for data quality, outlier detection, faulty/malicious info, data sanitation, etc.

4. Incentives for data/algorithm sharing, Existing data, projects (knowing what we don’t know) Portal for Data, tools, projects, use-cases, studies, related research Library of models, test suites, test beds, simulation tools Benchmarks for validation (worst/best cases) What does the community lack? (2)

5. Major Issues Privacy, security – Understanding the impact of anon. (etc.) algorithms on data/study quality – Anon algorithms Data fusion – Existing data sources, RITIS, STEWARD, mobile data, in-vehicle, etc. – Heterogeneity, time/space/context dependency, normalization, homogeneous formats, etc. Scale & other data issues – How much is enough, added value, granularity, application-dependent, transparency, private data providers/hiding

6. Opportunities/Directions Risk assessment, revisiting safety practices Interaction/collaboration with companies Breakthrough research – Long term – Non incremental (aiming for the sky) Feedback integration – Actuation – decision making – recommendations Going global