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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.