Alpamayo 1 is a Vision-Language-Action (VLA) model designed for autonomous driving applications. It implements the Alpamayo-R1 architecture that combines a Qwen3-VL backbone for vision-language reasoning with a diffusion-based action expert for trajectory prediction. The system takes multi-camera video and egomotion history as inputs, generates Chain-of-Causation (CoC) reasoning traces, and outputs future trajectories (6.4s horizon at 10Hz). Key components include a unicycle kinematic model for action space conversion, CUDA graph acceleration for efficient repeated inference, and robust geometric transformations. While the repository is noted to receive only limited maintenance as newer versions exist, the current implementation demonstrates strong engineering practices with several reusable components.
This repository provides the Bench2Drive benchmark for closed-loop end-to-end autonomous driving, accepted at NeurIPS 2024 Datasets and Benchmarks Track. It includes a large-scale multi-modal dataset (up to 7.3TB) collected in the CARLA simulator, an evaluation suite with metrics like Driving Score and Success Rate, and baseline results for over 10 methods. The codebase is a hybrid of research data pipeline and application: the dataset generation and evaluation scripts form the core data_research component, while the B2DVisualize Flask app, scenario runner, and leaderboard evaluator provide application-like interfaces for interaction and reproducibility. Data lineage: raw simulation data (sensor recordings, ground truth annotations) is collected via data_collect.py, transformed into compressed archives with JSON manifests, and evaluated using the leaderboard pipeline that computes multiple ability metrics. The repository answers research questions about the effectiveness of end-to-end driving policies under diverse urban scenarios.
This repository contains the research code for UniDriveVLA, a unified Vision-Language-Action (VLA) model for autonomous driving. It couples a Qwen3-VL based understanding expert with sparse 3D perception and trajectory planning experts via a Mixture-of-Transformers design. The codebase supports two parallel experiment tracks: nuScenes (real-world open-loop evaluation) and Bench2Drive (CARLA closed-loop simulation). It includes full training pipelines (staged pretraining on driving QA datasets, then fine-tuning on nuScenes/Bench2Drive), evaluation harnesses for detection, motion, planning, and VQA, and a CARLA leaderboard integration for scenario-based testing. The repository is primarily a research artifact—it is not a production application but rather a comprehensive release of model definitions, training scripts, data converters, and evaluation metrics for reproducing the paper's results. Significant code duplication exists between the nuScenes and Bench2Drive directories, and many paths are hardcoded, reducing portability. The code demonstrates strong engineering for research (custom CUDA ops, distributed training with DeepSpeed, modular mmdet3d plugins) but lacks the polish of a reusable library.