About the role
AI summarisedThis is a senior research scientist role focused on developing intelligent transportation solutions, specifically for next-generation urban road usage charging systems using GNSS. The project aims to enhance system resiliency against GNSS signal disruptions, detect malicious activities like jamming and spoofing, and improve vehicle positioning using AI and sensor fusion. The role involves algorithm development, data analysis, and field trials.
ResearchFull-time
Key Responsibilities
- Develop algorithm to detect jamming, spoofing, and blocking for GNSS data using hierarchical fusion of different sensors data collected from a vehicle OBU.
- Tracking algorithm to positively identify if users are driving on the roads and possibly engaging in malicious activities.
- Develop INS based dead reckoning algorithm using Kalman Filter variants.
- Design methods for extraction of map features and blind search to identify road segments based on INS-only data.
- Work on AI based multimodal data fusion and tracking algorithms for robust, high accuracy, real-time vehicle positioning in GNSS-challenged urban environments using multimodal data including GNSS, INS, V2X and others.
- Design test cases and conduct field trials to validate the algorithm performance for detection of malicious activities and improved vehicle positioning including problematic sites like carparks, urban canyon, tunnel, etc.
- Experience in large-scale data mining and spatio-temporal analysis of raw GNSS data from road users to characterize signal quality and positioning performance across the road network.
- Derive high quality road segments by analysing attributes like DOP, SNR, and snapping distances to map links.
- Develop algorithms for charging point design, selection and quality assessment.
- Conduct root cause analysis for low accuracy charging points.
Requirements
- Strong software development skills in C++ and Python.
- Hands-on experience with system design, modular architecture, and interfacing between multiple components.
- Familiarity with AI-assisted development workflows, including the use of AI tools for software development lifecycle (SDLC) automation to accelerate solution development.
- Ability to engage with stakeholders to formulate a comprehensive list of use cases and system requirements.
- Experience in system architecture design for integrated software solutions for the research outcomes.
- Expertise in data analytics and signal processing algorithms, with demonstrated ability to apply machine learning and AI techniques to real world problems.