---
title: Vla4ad
emoji: 🔥
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.1.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: 'Vision-Language-Action Models for Autonomous Driving: Past'
---

# Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future

## Introduction

The pursuit of fully autonomous driving (AD) has long been a central goal in AI and robotics. Conventional AD systems typically adopt a modular "Perception-Decision-Action" pipeline, where mapping, object detection, motion prediction, and trajectory planning are developed and optimized as separate components.

While this design has achieved strong performance in structured environments, its reliance on hand-crafted interfaces and rules limits adaptability in complex, dynamic, and long-tailed scenarios.

This survey reviews **Vision-Language-Action (VLA)** models — an emerging paradigm that integrates visual perception, natural language reasoning, and executable actions for autonomous driving. We trace the evolution from traditional **Vision-Action (VA)** approaches to modern VLA frameworks. Charting the evolution from precursor VA models to modern VLA frameworks, we provide historical context and clarify the motivations behind this paradigm shift.

## Definition

**Vision-Action (VA)**:
A vision-centric driving system that directly maps raw sensory observations to driving actions, thereby avoiding explicit modular decomposition into perception, prediction, and planning. VA models learn end-to-end policies through imitation learning or reinforcement learning.

**Vision-Language-Action (VLA)**
A multimodal reasoning system that couples visual perception with large VLMs to produce executable driving actions. VLAs integrate visual understanding, linguistic reasoning, and actionable outputs within a unified framework, enabling more interpretable, generalizable, and human-aligned driving policies through natural language instructions and chain-of-thought reasoning.