Real-Time Brain Injury Prediction and Protection Framework for Intelligent Vehicles

Details

The advancement of intelligent vehicles (IVs) has significantly improved their collision avoidance capabilities under normal driving conditions, yet the complete prevention of accidents remains elusive. In scenarios where collisions cannot be avoided, real-time accurate prediction of occupant injuries becomes crucial. It guides IVs to select the least harmful collision conditions, minimizing injury risks. Traditional injury prediction methods, relaying on simplified indicators like the Maximum Abbreviated Injury Scale (MAIS), are difficult to provide a comprehensive assessment of occupant injuries. These conventional approaches, by using a single indicator, simplified the complex nature of human injuries. Particularly, different types of brain injuries, such as skull fractures, diffuse axonal injuries, and bleeding, pose varying levels of threat to human life, underscoring the need for more sophisticated prediction and mitigation strategies within IV safety systems. Accurate predictions of these injuries are not only critical for reducing immediate harm but also play a pivotal role in post-crash emergency responses and medical triage,

Objectives:

We are seeking a highly motivated PhD candidate to join our research project at the University of Sheffield, focusing on the development of intelligent vehicle safety strategies with a focus on brain injury prediction. This project aims to revolutionize the way intelligent vehicle safety systems protect occupants by integrating advanced predictive models and safety mechanisms specifically designed to mitigate brain injuries in traffic accidents. The core objectives include:

1. Based on existing car collision simulation and traffic accident database, using machine learning methods to establish the relationship between vehicle kinematics and occupant head kinematics.

2. Developing real-time predictive models for brain injury type and severity using human head finite element models (loaded by occupant head kinematics) and machine learning algorithm.

3. Establishing an intelligent car safety protection strategy framework that integrates protection priorities of different types of brain injuries. 

Collaborations:

This PhD project is in collaboration with Tsinghua University in China, which will provide access to a substantial road accident database and vehicle safety decision-making algorithms, playing a crucial role in our research. This PhD project will also receive supervision/support from Imperial College London for high-fidelity modelling of human head and advanced head injury criteria.

The applicant:

Applications are welcome from graduates with a biomechanics, mechanical engineering or computer science background. Other relevant STEM or engineering background or relevant industrial experience will be considered.

Applicants should hold or be completing this year a degree at a good level (2.1/1st or equivalent) in a related subject, e.g. biomechanics, mechanical engineering, computer science, and should be able to demonstrate good interpersonal and organisational skills. The expected start date for this project is September 2024.

Interested candidates are strongly encouraged to contact the project supervisor, Dr Xiancheng Yu ([email protected]) to discuss your interest in and suitability for the project prior to submitting your application.