# hustvl/VAD

[ICCV 2023 & ICLR 2026] VAD: Vectorized Scene Representation for Efficient Autonomous Driving

Repository: https://github.com/hustvl/VAD
Canonical: https://ross.abutalabs.com/products/hustvl-vad
Homepage: https://arxiv.org/abs/2303.12077
Language: Python
License: Apache-2.0
License Family: permissive
Topics: end-to-end, autonomous-driving
Last push: 2026-01-31T08:13:44+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 65, release rhythm 35, longevity 92
- inputs: {"age_days": 1294, "days_push": 214, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1362, forks 169 (observed 2026-08-28T04:04:30.192397+00:00)

## What it is
VAD is an end-to-end autonomous driving framework that models the driving scene as a fully vectorized representation of agents and map elements for trajectory planning. It includes VADv1 (ICCV 2023) and VADv2 (ICLR 2026), achieving state-of-the-art planning performance on nuScenes with faster inference than rasterized approaches.

## Use cases
- run end-to-end trajectory planning on nuScenes
- reproduce vectorized autonomous driving research results
- train an end-to-end driving model with vectorized scene representation
- evaluate planning safety and collision rates for autonomous vehicles
- integrate VADv2 probabilistic planning into a driving stack
- benchmark inference speed of end-to-end driving planners

## When to choose
- you need a fast, vectorized end-to-end planning baseline for autonomous driving research
- you want reproducible nuScenes planning benchmarks with pretrained models
- you are exploring VADv2-style probabilistic trajectory planning

## When to avoid
- you need a production-ready, safety-certified driving stack
- your project requires rasterized occupancy-based scene representation
- you lack GPU resources for training large perception-planning models

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, simulation
- domain: autonomous-vehicles, machine-learning, computer-vision, robotics
- platform: python
- tags: end-to-end-driving, vectorized-scene-representation, trajectory-planning, nuscenes, iccv-2023, research-code, vadv2, motion-planning, gpu, linux

## Member repositories
- hustvl/VAD (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:30.192397+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:41:30.680912+00:00, confidence not recorded.
  - readme: https://github.com/hustvl/VAD (fetched 2026-08-28T04:04:30.192397+00:00, sha 05778eb8c1fa)
  - homepage: https://arxiv.org/abs/2303.12077 (fetched 2026-08-29T11:59:11.404027+00:00, sha 919358915664)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T11:59:11.413110+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T11:59:11.416655+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T11:59:11.418373+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T11:59:11.414899+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.