01
Master's Thesis · Radar Perception
End-to-End AI Radar Signal Processing
Extended T-FFTRadNet with a dense radial-velocity head for joint detection, occupancy, and per-cell velocity prediction from range–Doppler radar.
Aditya Ramchandra Pataki
Robotics Software & Perception Engineer
I work across radar, LiDAR, ROS/ROS2 and embedded robotics, combining learning-based perception with hands-on systems integration. I’m currently building toward connected and cooperative autonomy — trajectory planning, CAVs and V2X.
Radar · LiDAR · ROS/ROS2 · Embedded systems · Autonomous machines · Learning: CAVs & V2X
LocationErlangen, Germany · Open to relocation across the EU
Work authorizationEU Blue Card eligible on the strength of a German degree
AvailabilityAvailable immediately
01 / Selected work
Engineering work, explained through decisions and evidence.
Four case studies across radar perception, LiDAR scene flow, robotics systems integration, and point-cloud evaluation—each focused on the problem, my contribution, and the outcome.
02
LiDAR Scene Flow
DirectFlowMatch
A deterministic, geometry-aware scene-flow method that estimates per-point motion from consecutive LiDAR scans without segmentation or clustering.
Preparing the interactive DFM trace…
03
Human-in-the-loop robotics
Teleoperation Systems Integration
Implemented 4G/5G teleoperation on an ADAS-equipped prototype vehicle — remote operation between two sites — using NVIDIA Jetson, ROS control, four-camera GStreamer feedback and end-to-end reliability debugging.
04
LiDAR Data Investigation
Point-Cloud Compression
Researched several preconditioning techniques for 3D point clouds to improve their compressibility, from image-based representations to structure-aware ZFP experiments.
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Stanford Bunny · PNG decoded (lossless)
02 / Work visualization
A busy urban junction, from two scans to complete motion.
A synthetic scenario built for this site — a five-way junction with traffic crossing it — so moving vehicles and flow direction stay inspectable. Drag to rotate and scroll to zoom; step from the input pair through the initial matching to the complete point-level match.
Synthetic scenario · Busy urban junction
From two scans to one complete point-level match.
Inspect the input pair, initial matching, and complete match.
Active view 00 · Input pair Two consecutive scans from a stationary sensor.
Preparing the interactive DFM trace…
Two fingers to rotate · pinch to zoom
03 / Technical stack
Tools for sensing, learning, and deploying robotic systems.
A focused stack grounded in the work shown across the case studies. Categories 01–05 are demonstrated in those case studies; category 06 is where I’m currently building depth.
01
Robotics
- ROS
- ROS 2
- GStreamer
02
Perception
- Radar
- LiDAR
- Computer vision
03
Learning
- PyTorch
- Transformers
- BEV prediction
04
Systems
- NVIDIA Jetson
- Linux
- C++
- Python
05
Engineering
- CMake
- Git
- Technical documentation
06
Currently learning
- Trajectory planning
- Motion planning
- CAVs
- V2X
04 / Selected outcomes
Four signals of practical engineering depth.
Results from research, systems integration, and technical evaluation.
Demonstrated live to Valeo’s division CTO, regional government representatives and press.
Fixed 0.75 confidence threshold on highway evaluation.
05 / Contact
Let’s build a system that has to work beyond the demo.
I’m based in Erlangen, Germany, open to relocation across the EU, and interested in robotics software, perception, teleoperation, sensor-data engineering and research-oriented engineering roles — particularly where the work moves toward connected and cooperative autonomy.