Inpercept
Inpercept
Intelligent perception for autonomous and connected vehicles
36 months
2021 to 2023
The challenge
By 2050, it is predicted that more than 70 per cent of the world’s population will live in large cities and, as a result, the effects of congestion problems, lack of capacity and space in urban centres, high levels of pollution, road safety problems, and people protection issues are expected to increase. Therefore, today all regional and municipal governments and public administrations around the world are aiming to develop sustainable and equal mobility supported by technology.
The solution
The project aims to limit human driving and control capabilities and improve traffic capacity and conditions by designing, developing and implementing key technologies capable of moving driving towards more and more automation. In line with the improvement of driving automation and the specific levels of autonomy, under which a vehicle can be classified, numbered from 1 to 5, the main objective is to reach levels higher than the current level 2 (L2) through the development of a vision platform, which allows to achieve a high impact on the mobility sector, addressing transversal aspects such as safety, autonomy and sustainability of future transport.
The intermediate objective is to achieve the correct implementation of the different types of technologies involved in the following differentiated parts of the project:
Data collection and processing
Communications and data sharing
Outcomes: Key results
- Use case definition methodologies
- Evaluation plan and evaluation analysis
Role of Enide in INPERCEPT
Technical Role
ENIDE takes on the crucial role of a Data Analysis and Impact Assessment Specialist, aligning with our expertise in providing insightful solutions through advanced data analysis and impact evaluation.
Data Analysis and Impact Assessment
Within the framework of the INPERCEPT project, ENIDE assumes a pivotal role in supporting the Use Case Definition, Evaluation, and Impact Assessment task. Our expertise is particularly focused on safety, autonomous driving, and cybersecurity domains. Collaborating closely with project stakeholders, we contribute to the refinement of methodological approaches, ensuring robust use case definition and thorough evaluation. Drawing upon our proficiency in data analysis, we play an integral role in constructing an impactful assessment of these aspects. Our efforts involve the implementation of the FESTA methodology, a recognized framework for evaluating the impact of Autonomous Vehicles. By embracing this strategic collaboration, we contribute substantively to the project's overarching objective of advancing safer, more autonomous, and secure urban mobility solutions.
