E2EAD23
* *End-to-End Autonomous Driving: Perception, Prediction, Planning and Simulation
* Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
* Contrastive Learning for Depth Prediction
* Correlation Pyramid Network for 3D Single Object Tracking
* DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection
* Improved Association Pipeline for Multi-Person Tracking, An
* Integrated Perception and Planning for Autonomous Vehicle Navigation: An Optimization-based Approach
* Lanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-based Anchor Paths
* LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
* Multi-Object Tracking by Self-supervised Learning Appearance Model
* Pixel-level Contrastive Learning of Driving Videos with Optical Flow
11 for E2EAD23