#nuScenes

This article explores the application of Graph Visual Question Answering in autonomous driving systems, particularly for the 2024 challenge. It uses datasets such as nuScenes and CARLA to develop a VLM-based baseline approach that combines Graph VQA with end-to-end driving solutions. The project seeks to simulate human reasoning in driving, offering a holistic framework for perception, prediction, and planning. It merges language models with autonomous systems for explainable planning and improved decision-making in self-driving vehicles. Learn about the project's novel methodologies and its impact on the field of autonomous vehicles.

Det3D is a 3D Object Detection toolbox providing ready-to-use implementations of algorithms like PointPillars and SECOND. It supports benchmarks such as KITTI and nuScenes and includes features like multi-dataset support, a variety of models, distributed training, SyncBN, TensorboardX, and flexible anchor dimensions, thus supporting research in 3D object detection.

Discover EFG, an adaptable deep learning framework offering capabilities for 3D object detection and tracking, suitable for diverse research applications with structured project templates. This framework supports advanced features, including COCO Panoptic Segmentation and Pytorch 2.0 compatibility. Recent code releases for ICCV2023 and CVPR2023 papers integrate methodologies like TrajectoryFormer and ConQueR. Optimized for extensive datasets such as Waymo and nuScenes, EFG achieves notable benchmark performances on NVIDIA A100 GPUs. It provides a versatile solution for researchers aiming for flexibility and efficiency in a progressive deep learning landscape.