How can we develop autonomous vehicles that can explain the decisions they take?
Understanding the decisions taken by an autonomous machine is key to building public trust in robotics and autonomous systems (RAS). This project will design, develop, and demonstrate fundamental AI technologies in real-world applications to address this issue of explainability.
The aim of the project is to build robots, or autonomous vehicles, that can:
- sense and fully understand their environment
- assess their own capabilities
- provide causal explanations for their own decisions
In on-road and off-road driving scenarios, the project team will study the requirements of explanations for key stakeholders (users, system developers, regulators). These requirements will inform the development of the algorithms that will generate the causal explanations.
The work will focus on scenarios in which the performance of traditional sensors (e.g cameras) significantly degrades or completely fails (e.g. in harsh weather conditions). The project will develop methods that can assess the performance of perception systems and adapt to environmental changes by switching to another sensor model or a different sensor modality. For the latter, alternative sensing devices will be investigated (incl. radar and acoustic sensors) which can guarantee robust perception in situations when traditional sensors fail.
The team has been extending their methods for interpreting and representing observations of the environment in human-understandable terms, which will allow end-users to comprehend algorithmic decisions. To this end, they have improved the way that traditional sensors (e.g. cameras, lasers) are used in complex and rare traffic situations. However, due to limitations of these sensors under certain environmental conditions (e.g. bad weather, poor illumination), the team has also investigated non-traditional sensors (e.g. radars, microphones) for understanding the drivable surface and for assessing the ability to measure the vehicle's ego-motion.
The team takes a more nuanced approach than solely relying on one type of sensor. Towards this, they have developed a system which facilitates introspection (knowing when we don’t know) using radar. They have recently released a radar dataset advocating for the increased exploitation of these unusual sensors, and will use the lessons learned here in planning of the collection and release of a SAX dataset capturing complex, challenging driving scenarios outside of already well-investigated urban environments.
The team undertaking off-road trials in December 2019 with a specially adapted ORI Land Rover, equipped with vision and Lidar sensors to gather data to test algorithms for localisation and perception for autonomy in challenging environments.
- Broome, M., Gadd, M., De Martini, D., and Newman, P. “On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability,” AI. Multidisciplinary Digital Publishing Institute (MDPI), November 2020
- De Martini, D., Gadd, M., and Newman, P. "kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation", in Sensors, Special Issue on Sensing Applications in Robotics, vol. 20, no. 21, p. 6002, 2020. Multidisciplinary Digital Publishing Institute (MDPI), October 2020
- Gadd, M., De Martini, D., Marchegiani, L., Newman, P., Kunze, L. “Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Workshop on Ensuring and Validating Safety for Automated Vehicles (EVSAV), (Las Vegas, NV, USA), October 2020
- Williams, D., De Martini, D., Gadd, M., Marchegiani, L., and Newman, P. “Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision,” in IEEE Intelligent Transportation SystemsConference (ITSC), (Rhodes, Greece), September 2020
- Kaul, P., De Martini, D., Gadd, M., Newman, P. “RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), June 2020
- Gadd, M., De Martini, D., Newman, P. “Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance” in IEEE/ION Position, Location and Navigation Symposium (PLANS), April 2020
- Barnes, D., Gadd, M., Murcutt, P., Newman, P., and Posner, I. The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2020.
- Saftescu, S., Gadd, M., De Martini, D., Barnes, D., and Newman, P. Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2020.
- Tang, TY., De Martini, D., Barnes, D., and Newman, P. RSL-Net: Localising in Satellite Images From a Radar on the Ground, in IEEE Robotics and Automation Letters (RA-L) 2020
- Dr Lars Kunze co-organised the 2nd Workshop on 3D-Deep Learning for Automated Driving at the IEEE Intelligent Vehicles Symposium (IV’2020) - Las Vegas, NV, United States, June 2020.
- Dr Lars Kunze is a co-author of Marina Jirotka’s talk “Towards Responsible Innovation in Autonomous Vehicles” at the Driverless Futures? research workshop on “The politics of autonomous vehicles”, 16-17 December 2019, University College London.
- Oxford Robotics Institute (ORI) at the University of Oxford