Reethika Chidirala

Researching methods to automatically generate labeled radar data for ADAS and autonomous driving. Developing cross-domain annotation techniques combining LiDAR, camera, and radar data. Implementing radar-based object recognition using deep learning for efficient labeling. Expanding radar data analysis tools with visualization features for validation and debugging.

Defined requirements for a radar communication analysis tool to ensure system accuracy. Developed the tool using Python (PyQt, pandas, numpy, matplotlib) for data analysis and visualization and created a user-friendly GUI(PyQt).

Performed ECU flashing for next-gen infotainment systems using DTS9. Coordinated and executed the relocation of test benches from old to new buildings, ensuring minimal disruption and improved setup efficiency. Supported hardware/software compatibility validation and documented all results in Confluence.

Configured dSPACE Scalexio HiL systems and validated I/O hardware for ECU testing. Developed models for I/O and sensor behavior using Matlab/Simulink; validated using multimeter and oscilloscope. Simulated CAN bus communication and monitored it via dSPACE ControlDesk and Vector CANalyzer. Conducted ECU flashing and collaborated with customers to adapt testing requirements.

Advanced platforms for Automotive Systems, AUTOSAR Architecture Automotive Networks, Formal specification and verification, Image Understanding