A Modular Software Framework for Autonomous Vehicles

Kai Li Lim, Thomas Drage, Roman Podolski, Gabriel Meyer-Lee, Samuel Evans-Thompson, Jason Yao Tsu Lin, Geoffrey Channon, Mitchell Poole, Thomas Bräunl

Research output: Chapter in Book/Conference paperConference paperpeer-review

4 Citations (Scopus)


Sof tware frameworks for autonomous vehicles are required to interface and process data from several different sensors on board the vehicle, in addition to performing navigational processes such as path planning and lane keeping. These can include a combination of cameras, LIDARs, GPS, IMU, and odometric sensors to achieve positioning and localisation for the vehicle and can be challenging to integrate. In this paper, we present a unified sof tware framework that combines sensor and navigational processing for autonomous driving. Our framework is modular and scalable whereby the use of protocol buffers enables segregating each sensor and navigation subroutine individual classes, which can then be independently modified or tested. It is redesigned to replace the existing sof tware on our Formula SAE vehicle, which we use for testing autonomous driving. Our testing results verify the suitability of our framework to be used for fully autonomous drives.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538644522
Publication statusPublished - 18 Oct 2018
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 26 Sept 201830 Sept 2018


Conference2018 IEEE Intelligent Vehicles Symposium, IV 2018
CityChangshu, Suzhou


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