A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driv ....A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driving models. The project will lead to two innovations: in theory design an attack detection and prevention ecosystem for autonomous driving and in application implement a safety analysis toolset for industry-scale autonomous systems.Read moreRead less
Context-aware verification and validation framework for autonomous driving. This project aims to enhance the reliability and safety of emerging self-driving vehicles, through a framework that supports the validation and verification of autonomous driving systems. This project expects to generate new knowledge in areas of software engineering, intelligent transport, and machine learning, using a multi-disciplinary research combining expertise from various fields. Expected outcomes of this project ....Context-aware verification and validation framework for autonomous driving. This project aims to enhance the reliability and safety of emerging self-driving vehicles, through a framework that supports the validation and verification of autonomous driving systems. This project expects to generate new knowledge in areas of software engineering, intelligent transport, and machine learning, using a multi-disciplinary research combining expertise from various fields. Expected outcomes of this project are a family of new context-aware techniques to verify and validate complex behaviours in autonomous driving. This should provide significant benefits, such as safe autonomous driving systems and the improved journey experience and security for road users.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC230100001
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Training Centre for Automated Vehicles in Rural and Remote Regions. The Centre will build skills and capability to test and deploy safe, socially acceptable, automated vehicles (AV) for rural, regional and remote Australian public roads, where manufacturing, agriculture, mining and defence industries face significant challenges of driver shortages, rising costs, long distances, rough roads, and environmental impacts. The centre will unite technology providers, regulators, government and end ....ARC Training Centre for Automated Vehicles in Rural and Remote Regions. The Centre will build skills and capability to test and deploy safe, socially acceptable, automated vehicles (AV) for rural, regional and remote Australian public roads, where manufacturing, agriculture, mining and defence industries face significant challenges of driver shortages, rising costs, long distances, rough roads, and environmental impacts. The centre will unite technology providers, regulators, government and end users with world-leading interdisciplinary researchers to create new human-AV systems, datasets, frameworks, case studies, platforms, and a vastly upskilled workforce. This will reduce transport costs, increase capacity, boost supply chain efficiency and resilience, improve road safety, and elevate Australian capability.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101181
Funder
Australian Research Council
Funding Amount
$403,775.00
Summary
Information Fusion for Tracking Objects in Large-Scale Sensor Network. This project aims to develop a mathematical framework to combine multi-modal information coming from multiple sensors. These mobile sensors will be spatially distributed over a large-scale area for the purpose of multi-object tracking. The main application of this framework is for cooperative perception for intelligent decision making. Expected outcomes include a novel technique to integrate receiving information from multipl ....Information Fusion for Tracking Objects in Large-Scale Sensor Network. This project aims to develop a mathematical framework to combine multi-modal information coming from multiple sensors. These mobile sensors will be spatially distributed over a large-scale area for the purpose of multi-object tracking. The main application of this framework is for cooperative perception for intelligent decision making. Expected outcomes include a novel technique to integrate receiving information from multiple mobile agents (e.g. vehicle) to enhance their ability to anticipate situations in dynamic environments and to act effectively to enhance safety. This should provide benefits for the development of cooperative autonomous driving to enhance road safety.Read moreRead less