Jun 06, 2017 Toyota, RoadSafety, AutonomousDriving, SelfDrivingCars, DriverHealth, InCarHealthMonitoring
The automaker explains that previous CSRC programmes have yielded important advances in safety technology, which include systems that can predict and detect serious illness in drivers such as heart attacks. It can also monitor the performance of drivers with insulin-dependent diabetes. In addition, CSRC research has led to the use of intelligence that is helping develop and refine international standards.The new investment, CSRC Next, will support research over five years and follows four themes: The potential integration of active and passive safety systems, using advanced pre-crash sensors to improve and personalise crash protection; Building advanced vehicle-user experience models for individuals and society to improve usability and strengthen the relationship between driver and vehicle; Improving mobility by studying how to detect the driver's personal condition, using physiology and health metrics; and Applying big data and safety analytics techniques to develop algorithms and tools to study naturalistic driving data.The new research portfolio includes eight projects to be conducted in partnership with six academic institutions. It includes work with the Massachusetts Institute of Technology's AgeLab to develop new systems for autonomous vehicles to perceive and identify objects in their environment and understand social interactions in traffic.Another study with Virginia Tech is designed to estimate issues that may arise following the deployment of a car's integrated safety systems. CSRC is working with the Toyota Research Institute and Toyota Connected to help accelerate the development of autonomous driving technology and explore the complex relationship between future mobility and broader social trends.In-vehicle health monitoringEmergency medicine has been one of the key beneficiaries of CSRC research, including ways of detecting the onset or presence of serious health conditions that might affect a driver's performance.A project conducted with the University of Michigan studied a computational technique for robust detection and prediction of people suffering severe heart complaints inside a vehicle, such as a myocardial infarction (heart attack) or ischemia (coronary artery disease). ECG data collected from subjects in hospitals and vehicles will be combined with machine learning models to predict and recognise cardiac events.Another study, in collaboration with the University of Nebraska, used real-time glucose monitoring systems in drivers with insulin-dependent diabetes. The aim was to investigate the feasibility of combining physiological and driving sensor data to determine levels and patterns of glucose control that might produce changes in behaviour or safety.
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