Naturalistic Driving Data Analytics
The 9th International Workshop on Naturalistic Driving Data Analytics (NDDA)
Sponsored by IEEE ITSS Technical Committee on Data Analytics and Intelligent Systems for Advanced Driving and Mobility (DAISY)
IEEE Intelligent Vehicles Symposium - IV 2022
June 5, 2022 | Aachen, Germany
Theme: Emerging Opportunities in NDDA
This year's workshop will be held at the Eurogress Aachen Conference Center (https://www.eurogress-aachen.de/en/congresscenter) with hybrid format.
For in-person participants: Room K8, Eurogress Aachen
For online participants: Zoom virtual meeting room
Note: The demonstration during the break will only be available to in-person participants. It will not be broadcasted online.
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.
- Data collection
- Data analytics
- Naturalistic Driving applications
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.
Duration: 8:30 AM - 12:30 PM Local Time (German Time)
Risk Assessment of Highly Automated Vehicles with Naturalistic Driving Data: A Surrogate-based Optimization Method
Trusting Explainable Autonomous Driving: Simulated Studies
From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles’ Actions