A Software Architecture for an Autonomous Racecar

This paper presents a detailed description of the software architecture that is used in the autonomous Roborace vehicles by the TUM-Team. The development of the software architecture was driven by both hardware components and usage of open source languages for making the software architecture reusable and easy to understand. The architecture combines the autonomous software functions perception, planning and control which are modularized for the usage on different hardware and for the purpose of using the car on high speed racetracks. The goal of the paper is to show which software functions are necessary for letting the car drive autonomously and fast around a racetrack.

[1]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[2]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[3]  ZuWhan Kim,et al.  Robust Lane Detection and Tracking in Challenging Scenarios , 2008, IEEE Transactions on Intelligent Transportation Systems.

[4]  Markus Lienkamp,et al.  What can we learn from autonomous level-5 motorsport? , 2018, Proceedings.

[5]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[6]  Shinpei Kato,et al.  An Open Approach to Autonomous Vehicles , 2015, IEEE Micro.

[7]  Adam Milstein,et al.  Occupancy Grid Maps for Localization and Mapping , 2008 .

[8]  Mohan M. Trivedi,et al.  Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation , 2006, IEEE Transactions on Intelligent Transportation Systems.

[9]  Dirk Langer,et al.  Up to the limits: Autonomous Audi TTS , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[10]  J. Christian Gerdes,et al.  Path-tracking for autonomous vehicles at the limit of friction , 2017, 2017 American Control Conference (ACC).

[11]  Cyrill Stachniss,et al.  Simultaneous Localization and Mapping , 2016, Springer Handbook of Robotics, 2nd Ed..

[12]  Silvio Savarese,et al.  Combining 3D Shape, Color, and Motion for Robust Anytime Tracking , 2014, Robotics: Science and Systems.

[13]  Marcelo H. Ang,et al.  Perception, Planning, Control, and Coordination for Autonomous Vehicles , 2017 .

[14]  Naoki Suganuma,et al.  Localization for autonomous driving on urban road , 2015, 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).

[15]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[16]  Sebastian Thrun,et al.  Precision tracking with sparse 3D and dense color 2D data , 2013, 2013 IEEE International Conference on Robotics and Automation.

[17]  Sebastian Thrun,et al.  Robust vehicle localization in urban environments using probabilistic maps , 2010, 2010 IEEE International Conference on Robotics and Automation.

[18]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[19]  In-Soo Lee,et al.  Real-time lane detection and tracking system using simple filter and Kalman filter , 2017, 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN).

[20]  Shinpei Kato,et al.  Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems , 2018, 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).