A Methodology to Identify Relevant Use-Cases and Safety-Critical Scenarios

The i4Driving project aims to create a modular simulation library that captures the complexity of road traffic, managing uncertainties in human behaviours and driving scenarios. This approach will support stakeholders in developing, testing, and validating automated driving systems, ensuring safety in mixed traffic with Automated Vehicles (AVs). The i4Driving methodology paves the way for more robust and resilient CCAM systems, offering a proposition for AV licensing in the future.
To do so, it is necessary to develop a methodology for identifying users, use cases, and scenarios for the i4Driving project. The process involves interviews with relevant partners, representing various user groups such as Academia, Insurers, Test Centres, Regulators, Tier 1 suppliers, and more.

Can simulation predict traffic safety indicators? Transition from “No” to “Maybe” with the i4Driving human driver model

The development of Connected and Autonomous Vehicles (CAV) and Advanced Driver Assistance Systems (ADAS) is rapidly progressing. As these technologies become more prevalent on our roads, guaranteeing their safety becomes of critical importance. The complex and dynamic nature of CAVs and ADAS demands rigorous safety assessments to address potential risks and hazards. These assessments involve comprehensive testing, simulation and validation processes to evaluate their reliability, functionality and ability to interact with other road users and infrastructure seamlessly. i4Driving’s second innovation is to augment available models with a 4D cognitive layer, to make the most of unveiled patterns and existing behavioural and psychological theories, which is the focus of this blog post.

Statistical Learning for Driving Behaviour Profiling

i4Driving’s first innovation, and the focus of this blog, is to develop state-of-the-art data mining techniques to unveil patterns and formulate plausible hypotheses from Naturalistic Driving Studies (NDS) and Driving Simulation Experiments (DSE) datarelated to human (and external) factors and driving behaviours, which will be used identify model requirements.

Reaching the oasis offered by autonomous vehicles – SwissRe Blog

The i4Driving project, with Swiss Re among 17 partners, aims to set an industry standard for assessing autonomous vehicle safety over the next three years. Swiss Re’s involvement highlights the need for a new risk assessment paradigm, contributing to an insurance framework for public protection in AV accidents and paving the way for a tangible future with autonomous vehicles