Navya Priya Prudhvi
Automotive System Validation Engineer – Infotainment & ADAS • Bengaluru, Karnataka • n***************@gmail.com • +91*******358 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Automotive System Validation Engineer with 3.9 years of experience in ADAS and Infotainment ECU validation. Experienced in CAN/CAN-TP/UDS diagnostics, Python and CAPL automation, HIL closed-loop simulation using Vector VT System and dSPACE, and ASPICE-aligned V-Model validation for global OEMs. Delivered automation that reduced regression effort by 35% and implemented system-level integration testing, ECU flashing, communication debugging, and real-time simulation to improve defect detection and shorten test cycles.
Technical Skills
Programming Languages: Python
Cloud and DevOps: Linux
Testing: Vector CANoe,Vector CANalyzer,Vector Flash,IBM DOORS,Python-based automation frameworks,CAPL-based test automation,HIL integration for automated regression
Tools and Methodologies: ASPICE SWE.5,V-Model,AUTOSAR,JIRA
Skills: CAPL
Automotive Protocols: CAN ISO 11898,CAN-TP ISO 15765-2,UDS ISO 14229,Automotive Ethernet basics
HIL & Simulation: Vector VT System,dSPACE ControlDesk, ConfigurationDesk,DYNA4,vTESTstudio,AutomationDesk
Infotainment & Connectivity: Android Auto,Apple CarPlay,Bluetooth HFP, A2DP, AVRCP, PBAP, MAP,Telematics,Navigation
Diagnostics & Debugging: UDS services session, security, read,write DID, DTC operations,DLT Viewer,ADB debugging,Tera Term,CAN log & trace analysis
Operating Systems: QNX,Android,Windows
Other: ECU flashing & variant coding,e-Bus Player
Work Experience
Tata Consultancy Services
Bengaluru, Karnataka
System Engineer
May 2022 – Jan 2026
At Tata Consultancy Services supporting automotive OEM clients (Jaguar Land Rover, Ford) delivering ADAS and infotainment ECU validation, HIL integration, diagnostics, and automation.
Tech Stack: Vector VT System, dSPACE ControlDesk, ConfigurationDesk, DYNA4, vTESTstudio, AutomationDesk, Python, CAPL, Vector CANoe, Vector CANalyzer, IBM DOORS, JIRA, DLT Viewer, Vector Flash, ADB debugging, e-Bus Player
- Performed end-to-end ADAS ECU system validation on Vector VT System and dSPACE HIL with DYNA4 closed-loop simulation, validating Adaptive Cruise Control, Surround View Camera and Hill Hold Assist across dynamic driving scenarios to improve system-level defect discovery.
- Developed Python and CAPL automation for CAN signal validation and test execution across ADAS and infotainment suites, reducing manual regression effort by 35% and increasing repeatability of system tests.
- Executed UDS-based diagnostic validation (0x10, 0x22, 0x2E, 0x27, 0x19) including DTC verification, DID read/write and NRC analysis to accelerate root-cause identification and support ECU fault-recovery validation.
- Conducted functional, system and regression testing for infotainment (Android Auto, Apple CarPlay, Bluetooth profiles, navigation, telematics); designed automated test cases that improved regression coverage and reduced manual test time.
- Performed ECU flashing, variant coding and bench bring-up; used Vector CANoe and CANalyzer for CAN network simulation and signal-level debugging, and reported/tracked 100+ defects in JIRA with ASPICE-aligned requirement traceability.
- Designed and integrated scalable automation and HIL test logic using vTESTstudio and AutomationDesk to enable continuous regression runs, reducing regression cycle time from 5 days to 3 days while maintaining traceability in IBM DOORS.
Projects
ADAS ECU Validation & HIL Automation (Jaguar Land Rover) | Oct 2023 – Dec 2025
Tools Used: Vector VT System, dSPACE, DYNA4, Python, CAPL, CANoe, UDS
- Performed system-level validation of ADAS ECUs on HIL for real-time vehicle behavior and dynamic scenarios validating ACC, Surround View and HHA.
- Developed Python and CAPL automation for CAN signal validation and test execution, reducing regression effort by 35%.
- Validated diagnostic workflows and UDS services including DTC and DID verification and NRC-based fault analysis to improve troubleshooting.
- Configured closed-loop HIL with DYNA4 integrated into VT System to replicate vehicle dynamics for more accurate validation.
- Analyzed CAN logs and traces to perform root-cause analysis and improve defect resolution turnaround.
Infotainment System Level Testing (Ford Motors) | Jun 2022 – Sep 2023
Tools Used: Python, CAPL, Vector CANoe, CANalyzer, Android Auto, Apple CarPlay, Bluetooth
- Executed functional, system and regression testing (black-box and grey-box) for infotainment ECUs across multiple vehicle platforms.
- Validated connectivity and media features including Bluetooth (HFP, A2DP, AVRCP, PBAP, MAP), Android Auto, Apple CarPlay, navigation and telematics.
- Developed and executed Python-based automation scripts to improve regression coverage and reduce manual testing effort by 35%.
- Performed UDS-based diagnostics for read/clear DTC operations and DID validation to support debugging and issue isolation.
- Conducted ECU flashing, variant coding and bench bring-up activities to support integration and release readiness.
Education
Narayana Engineering College, JNTUA - Nellore
Bachelor of Technology in Electronics and Instrumentation Engineering • Nellore, Andhra Pradesh • 2017 – 2021
Certifications
Python Certification — HackerRank
Achievements
- Identified critical & high-severity defects in ADAS and infotainment systems: Discovered issues that prevented potential production problems and improved system safety and reliability.
- Client recognition for communication and delivery: Acknowledged by clients for clear defect discussions and commitment to delivery timelines.
- Improved test efficiency through automation: Enhanced coverage and reduced manual effort by implementing automation-driven validation strategies.
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