SparseDrive is an end-to-end autonomous driving framework that processes multi-view camera images to perform unified perception and planning. It simultaneously predicts 3D object bounding boxes with tracking IDs, vectorized HD map elements (dividers, boundaries, pedestrian crossings), agent motion trajectories, and ego vehicle planning trajectories. The architecture employs a ResNet-50 FPN backbone, deformable feature aggregation for multi-view fusion, instance banks for temporal object representation, and specialized heads for each task. Key innovations include hierarchical planning with collision-aware rescore and symmetric sparse representation for detection, mapping, and motion tasks. The system is designed for efficiency, achieving 9.0 FPS on NVIDIA A100 with significantly reduced training time compared to prior art like UniAD.