Validated real-world metrics from our campus deployment testing, covering SLAM localization, perception accuracy, and behavioral prediction.
Performance benchmarks for environmental understanding.
Accuracy of multi-agent trajectory forecasting.
| Metric | Target Benchmark | Implementation Notes |
|---|---|---|
| Object Detection Accuracy | >90% Precision/Recall | Achieved via camera + radar fusion pipelines. |
| SLAM Map Update Rate | 10 Hz | Real-time integration of GPS, IMU, and Vision. |
| Collision Detection Rate | >95% Reliability | Dynamic obstacle detection for campus safety. |
| Multi-agent Prediction | >85% Accuracy | Trajectories predicted for pedestrians and vehicles. |
| Weather Robustness | >80% Reliability | Maintained through Radar + IMU redundancy. |
Advanced protection systems built into the core of the Svashasan Pilot suite.
The system applies the brakes automatically if a collision with a detected obstacle is imminent.
Alerts the driver to vehicles in adjacent lanes that may not be visible in side mirrors or cameras.
Automatically limits acceleration if the system detects an object directly in the car’s travel path.
