Rishikesh-Jadhav/Birds-Eye-View-Trajectory-Prediction-for-Autonomous-Driving
This repository contains our work on a comprehensive investigation on motion prediction for Autonomous Vehicles using the PowerBEV framework and a Multi-Camera setup. Validated trajectory forecasting capabilities on the NuScenes, Woven and Argoverse datasets and identified challenges in model generalization across these datasets.
This project helps automotive engineers and researchers developing autonomous driving systems to predict vehicle and pedestrian movements. It takes raw perception data from multiple cameras and LiDAR sensors (like those found in self-driving cars) and outputs detailed, bird's-eye-view forecasts of where other road users will be in the near future. This is crucial for planning safe and efficient autonomous vehicle maneuvers.
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Use this if you need to accurately forecast the trajectories of moving objects around an autonomous vehicle using multi-camera and LiDAR sensor data.
Not ideal if you are looking for a solution that generalizes perfectly across all autonomous driving datasets without any fine-tuning or adaptation.
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May 05, 2024
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