The 10th International Workshop on Naturalistic Driving Data Analytics (NDDA) 

IEEE Intelligent Vehicles Symposium - IV 2023

June 4, 2023 | Anchorage, Alaska, USA

Link for paper submission: https://edas.info/newPaper.php?c=30459&track=115618

Scope

Understanding driving characteristics, driver behaviors, vehicle performance characteristics, traffic environment and modalities in real world driving context are important for the development of future mobility applications for Intelligent Vehicles. Driver engagement with the vehicle operation, driver-vehicle capabilities for handling demanding traffic situations, traffic management protocols and fuel efficiency improvements are some of the key research topics to address using naturalistic driving data.

Naturalistic Driving data collected from various onboard sensors, infrastructure sensors, and other emerging data sources provide a wealth of information pertaining to a snapshot of real-world driving context. However, these data streams are inherently heterogeneous due to multimodal nature of sensor suites and data collection platforms used. Therefore, our intention is to investigate intelligent data analytic approaches to produce meaningful inferences from real-world driving data for the safe deployment of intervening technologies for future mobility applications. 

Topics 

• Data collection

  • • Data needs and specifications for Naturalistic Driving data collection

    • Robust data compression and annotation techniques

    • Driver, driving, and environment data collection platforms including in-vehicle physiology sensing

• Data analytics 

  • • Advanced and automated driving systems data analytics

    • Understanding/ interpretation of naturalistic driver behavior and modeling

    • Driver state, collaborative driving algorithms

• Naturalistic Driving applications

  • • Driver-Vehicle Interaction, Driver- ADAS/ADS Interaction

    • Crash risk analysis, reconstruction, modeling, and intervention using naturalistic driving data

    • Integration of naturalistic driving models for scenario generation for the safety evaluation of Automated Driving systems

    • Assessment protocols for driver health and wellness using naturalistic driving data

    • Fuel efficiency

    • Collaborative, connected, and shared traffic management systems 

Objectives

• Understand driver behaviors in various levels of automation from naturalistic driving data.

• Analyze driving conditions and driver-vehicle performance for safety applications and future mobility solutions.

• Explore driving performance metrics for early diagnosis of health issues. 

• Requirement analysis for future applications in automated-connected driving, traffic management, and infrastructure design for future mobility. 

MobiScout: Next-Generation Naturalistic Driving Data Collection and Analytics to Harness Industry 4.0 Revolution

First-generation real-world driving data started using single-instrumented vehicles, in which multiple drivers on a set path drove and recorded their driving data at high resolution. Then, in the second step revolution, data could be collected at a reasonable cost using the driver's car using edge computing and storage. Both systems' data were brought into a central on-situ server to protect the PII data. In general, the video data were manually annotated near the interest points. Hand coding of data has resulted in several interesting discoveries. But with the Industry 4.0 revolution changing several industries, the time has come to update each step of the traditional NDS data collection process. With MobiScout platform, we have started using Industry 4.0 technologies such as cloud computing, fast connectivity, edge processing, Artificial engineering, and crowdsourcing to upgrade how NDS analytics is conducted. This talk will highlight our experience in Data Collection, Data Analytics, Data Sharing, and Crowd Sourcing. We will also present our vision for future NDS studies.

Organizers