About the role
AI summarisedThis project focuses on developing resilient and accurate vehicle positioning systems for next-generation urban road usage charging. The role involves creating data-driven tools to manage charging points, designing frameworks to detect malicious GNSS signal interference (jamming, spoofing, blocking), and leveraging AI/sensor fusion for robust positioning in challenging urban environments.
ResearchOnsite
Key Responsibilities
- Develop algorithms to detect jamming, spoofing, and blocking of GNSS data through hierarchical fusion of multi-sensor data from a vehicle OBU.
- Develop an INS-based dead reckoning algorithm utilizing Kalman Filter variants and methods for map feature extraction from INS-only data.
- Implement AI-based multimodal data fusion and tracking algorithms for high-accuracy, real-time vehicle positioning in GNSS-challenged urban settings using GNSS, INS, and V2X data.
- Conduct large-scale data mining and spatio-temporal analysis of raw GNSS data to characterize signal quality and positioning performance across the road network.
- Derive high-quality road segments by analyzing attributes like DOP, SNR, and snapping distances to map links.
- Develop algorithms for charging point design, selection, quality assessment, and root cause analysis for low-accuracy charging points.
- Design test cases and conduct field trials to validate algorithm performance for malicious activity detection and improved vehicle positioning in difficult locations (e.g., tunnels, urban canyons).
Requirements
- Strong software development skills in C++ and Python.
- Hands-on experience with system design, modular architecture, and interfacing between multiple software components.
- Expertise in data analytics and signal processing algorithms, with demonstrated application of ML/AI to real-world problems.
- Experience in large-scale data mining and spatio-temporal analysis of raw GNSS data.
- Ability to engage with stakeholders to define use cases and system requirements.
- Experience in system architecture design for integrated software solutions derived from research outcomes.