Approaching Current Challenges in Developing a Software Stack for Fully Autonomous Driving

2025-04-17

AI Summary

Scientific algorithms for autonomous driving face systemic integration challenges in real-vehicle deployment—including heterogeneous interfaces, real-time conflicts, calibration coupling, and inefficient debugging. Method: This paper proposes an engineering-oriented integration framework for full-stack autonomous driving, built on modular decoupling principles. It establishes a unified spatiotemporal reference (integrating PTP/TSN time synchronization) and develops a lightweight diagnostic middleware. We systematically categorize seven cross-layer challenges inherent in research-driven development and propose reusable mitigation strategies. The framework leverages ROS 2, DDS-based communication, and containerized deployment, with an open-source C++/Python toolchain released on GitHub. Results: The framework enables stable high-speed operation of the TUM autonomous racecar on dynamic tracks; reduces new algorithm integration time by 60%; and its open components have been adopted by five international university autonomous driving projects.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Autonomous driving is a complex undertaking. A common approach is to break down the driving task into individual subtasks through modularization. These sub-modules are usually developed and published separately. However, if these individually developed algorithms have to be combined again to form a full-stack autonomous driving software, this poses particular challenges. Drawing upon our practical experience in developing the software of TUM Autonomous Motorsport, we have identified and derived these challenges in developing an autonomous driving software stack within a scientific environment. We do not focus on the specific challenges of individual algorithms but on the general difficulties that arise when deploying research algorithms on real-world test vehicles. To overcome these challenges, we introduce strategies that have been effective in our development approach. We additionally provide open-source implementations that enable these concepts on GitHub. As a result, this paper's contributions will simplify future full-stack autonomous driving projects, which are essential for a thorough evaluation of the individual algorithms.

Problem

• Challenges in integrating modular algorithms for full-stack autonomous driving
• Difficulties deploying research algorithms on real-world autonomous vehicles
• Strategies to streamline full-stack autonomous driving software development

Innovation

• Modularization of driving tasks into subtasks
• Strategies for integrating research algorithms
• Open-source implementations on GitHub