# Engineering Autonomous Driving Software

arXiv: https://arxiv.org/abs/1409.6579 · HTML: https://mlsanity.com/paper/1409.6579/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2014.

## Built on by (most cited first, 3 of 3)

- [Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models](https://mlsanity.com/paper/2504.11514/) (2025)
- [XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis](https://mlsanity.com/paper/2406.18360/) (2024)
- [Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving](https://mlsanity.com/paper/2210.08885/) (2022)

## Abstract (arXiv)

A larger number of people with heterogeneous knowledge and skills running a project together needs an adaptable, target, and skill-specific engineering process. This especially holds for a project to develop a highly innovative, autonomously driving vehicle to participate in the 2007 DARPA Urban Challenge. In this contribution, we present essential elements of a software and systems engineering process to develop a so-called artificial intelligence capable of driving autonomously in complex urban situations. The process itself includes agile concepts, like a test first approach, continuous integration of all software modules, and a reliable release and configuration management assisted by software tools in integrated development environments. However, one of the most important elements for an efficient and stringent development is the ability to efficiently test the behavior of the developed system in a flexible and modular system simulation for urban situations both interactively and unattendedly. We call this the simulate first approach.