Autonomous Vehicle Corner Cases Discovery and Synthesis

Brief description of the context of the research including potential impact:
Computer vision and robotics tasks often rely on methods trained or tuned in a specific setting but are deployed in different conditions. We currently lack good ways to quantify the difference between sets of sensor data, such as images. Developing methods to measure such domain gaps could benefit many tasks, such as verifying that an autonomous vehicle is gathering sensor data in the expected domain for it to be deployed or measuring how biased image datasets used for training computer vision models are.

Aims and Objectives:
This work will create a general framework to generate compact representations of datasets that can be efficiently compared while keeping sensitivity to diverse levels of attributes (e.g., colour profile and semantic content of images). The framework will also include an approach to create a custom comparison of the representations adapted to specific applications.
The framework will be validated by demonstrating its use in several existing well-defined research tasks and by developing novel applications enabled by the new capability provided by the framework.

Novelty of the research methodology:
While many research areas focus on training models that are robust to domain gaps or adapting models to new domains, there are no widely accepted approaches to measure the domain gaps themselves.
The framework we envision will leverage recent advances in learning general representations (e.g., foundation models) with more classical statistics to generate general and efficient representations of datasets that are easily comparable. Our framework will be relevant to diverse research domains affected by domain gaps.
Our framework will provide a new capability that we hope will enable novel robotics and computer vision tasks.

Alignment to EPSRC's strategies and research areas:
This project relates to the EPSRC's research areas of image and vision computing, information systems, databases, digital signal processing, artificial intelligence, and robotics.

Companies or collaborators involved:
We will conduct the project in collaboration with industry partner Oxa.

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.