Results
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Public Deliverables
D1.3: Data Management Plan (final)
D1.4: Regulatory, societal, ethical and gender issues report
D2.1: SoA on data spaces & secure information sharing
D2.2: User Requirements and Use Cases
D2.3: System Technical Specifications and PLIADES Framework Architecture
D2.4: Security and privacy issues related to data spaces
D2.5: Human factors across data life cycles in dataspaces
D3.3: Sustainable data generation and refinement for mobility, industrial, energy, and healthcare
D3.4: Mechanisms and techniques for injecting human knowledge into the data creation process
D3.5: Extending data re-use capacity through AI-enabled data elaboration methods
D3.7: Cross-domain data Governance ensuring Quality and Legal adherence
D4.1: Multi-dataspace integration techniques for efficient data management and re-use
D4.3: Augmenting mobility intelligence through Privacy-preserving data sharing and analysis techniques
D4.7: GDPR Compliance and Enhanced Privacy for Mapping Personal Data Flows
D5.1: Unified Abstraction Framework for Enhancing Interoperability across Multiple Data Spaces
D5.2: Seamless Cross-Domain Data Exchange for Enhanced Data Interoperability
D5.5: Empowering Strategies for Inclusive AI Model Development and Sustainable Data Maintenance
D5.6: Robust AI-Enhanced Data Transformation Ensuring Secure and Privacy-Preserving Data Sharing
D6.5: Cross-source and AI-Enabled data quality assessment and improvement strategies
D7.1: System demonstration, pilots Specification and pilot sites preparation plan
D8.1: Dissemination and communication plan
D8.2: Dissemination and Communication Activities Report (v1)
D8.3: Dissemination and Communication Activities Report (v2)
D8.4: Dissemination and Communication Activities Report (v3)
D8.5: Report on European Standardization Policy and Sustainability Landscape Analysis
D8.8: Report on European Interoperability Framework Contributions
D8.9: Report on European Interoperability Framework Contributions (final)
Publications
Abstract: The existence of large-scale datasets for various autonomous driving tasks has created an increasing need for more automated annotation processes. Especially for safety critical tasks related to vehicle-pedestrian interaction, detailed and time-consuming human-made annotation is required, in order to assure accurate perception throughout any type of operating environment and for challenging conditions. In this paper, we present an automated method for the annotation of actions of humans crossing or not crossing the road. Firstly, we utilize a highly-accurate 3D multi-object tracking pipeline that combines RGB images and LiDAR data to extract the velocity and direction of movement of each pedestrian in the surrounding environment. A drivable area extraction neural network is then utilized to segment the traversable area around the vehicle. The correlation between the two above-mentioned components in the 3D space provides an accurate indication, regarding the pedestrian crossing or not-crossing the road ahead of the vehicle. Our method is validated using a custom-made multimodal dataset with an autonomous vehicle in various scenarios of a semi-structured area. The auto-generated annotations are compared directly with the human-made labels of multiple annotators and showcase the effectiveness of our method to provide an accurate indication about the human crossing the road action.
