# A Baseline for the Commands For Autonomous Vehicles Challenge

arXiv: https://arxiv.org/abs/2004.13822 · HTML: https://mlsanity.com/paper/2004.13822/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models](https://mlsanity.com/paper/2312.03543/) (2023)
- [Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving](https://mlsanity.com/paper/2305.15765/) (2023)
- [Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?](https://mlsanity.com/paper/2106.04232/) (2021)
- [Grounding Commands for Autonomous Vehicles via Layer Fusion with Region-specific Dynamic Layer Attention](https://mlsanity.com/paper/2203.06822/) (2022)

## Abstract (arXiv)

The Commands For Autonomous Vehicles (C4AV) challenge requires participants to solve an object referral task in a real-world setting. More specifically, we consider a scenario where a passenger can pass free-form natural language commands to a self-driving car. This problem is particularly challenging, as the language is much less constrained compared to existing benchmarks, and object references are often implicit. The challenge is based on the recent textttTalk2Car dataset. This document provides a technical overview of a model that we released to help participants get started in the competition. The code can be found at https://github.com/talk2car/Talk2Car.