Evolution of a Reliable and Extensible High-Level Control System for an Autonomous Car

Kai Li Lim, Thomas Drage, Chao Zhang, Craig Brogle, William W. L. Lai, Timothy Kelliher, Manuchekhr Adina-zada, Thomas Braunl

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)


The reliability of autonomous vehicles is heavily dependent on their software frameworks, which are required to interface and process data from many different sensors on board the vehicle, perform navigational processes such as path planning and lane keeping, take action to ensure safety and display data to an operator in a useful fashion. These sensors can include a combination of cameras, LiDARs, GPS, IMU, and odometric sensors to achieve positioning and localisation for the vehicle and nearby objects in their environment and can be challenging to integrate. In this paper, we present a hybridised software framework that combines sensor and navigational processing for autonomous driving. Our framework utilises a modular approach for interfacing and safety functionality, whilst navigation and sensor interfaces are implemented as nodes in the robot operating system. Our testing results verify the suitability of our framework by integration with a hardware-in-the-loop simulation system and for fully autonomous field driving.
Original languageEnglish
Article number8728265
Pages (from-to)396-405
Number of pages10
JournalIEEE Transactions on Intelligent Vehicles
Issue number3
Publication statusPublished - Sept 2019
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 26 Sept 201830 Sept 2018


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