AI4CCAM interview! Akkodis tells us more about what “impact” and “validation” really mean when dealing with AI for AV
AI4CCAM interviewed Pavan Vasishta, Akkodis, leader of the project WP4 working on “Use Case Implementation and Validation”.
Pavan is a Senior Research Scientist in Akkodis, and in this interview he tells us more what validation and impact mean when dealing with Artificial Intelligence (AI) for Autonomous Vehicles.
As leader of the WP4 of the project, what kind of work you did to define a validation process able to include a variety of CCAM use cases?
Our work in WP4 of the project deals mainly with validating the various AI models that will come out of this project in perception and trajectory prediction. Along with other project partners, we are developing guidelines on what validation means in terms of AI for Autonomous Vehicles.
For this, we are working on creating a Digital Twin – a recreation of the real world in simulation – that will act as a playground for all these models. Within this microcosm, we will be able to simulate a variety of behaviours, weather conditions and test out many different scenarios. Each use case and scenario will be studied in depth and simulated within the Digital Twin and compared against ethical and technological criteria for Vulnerable Road User acceptance of Connected and Autonomous Mobility.
What is the impact and the role of AI in the use cases you are working on within the project?
Explainable AI is at the heart of the use cases we are working on within the project. A major problem in the acceptance of AI today is its perceived “black box”-ness. One does not know what goes on within an AI model after inputting certain data. We aim to keep explainability at the heart of our work, especially when it comes to perception and trajectory prediction of VRUs.
While we are working on improving and validating Advanced Driver Assistance Systems and the robustness of AI-based perception systems for CAVs, we are also actively contributing to the development of trustworthy AIs in safe trajectory prediction. We have managed to get some very good results in predicting pedestrian pedestrian behaviour in urban scenarios.
How can AI4CCAM impact the user acceptance of CCAM let us say, in a 5-year horizon?
Autonomous Vehicles can be a game changer in human behaviour in the long run, providing autonomy, independence and safety to many, many people around the world. One of the main issues plaguing user acceptance is the opacity of vehicle behaviour and manoeuvres on open roads and in the presence of other road users. With all the work that we are putting into the explainabilty of the vehicles’ intentions in a variety of scenarios, within the ambit of AI4CCAM, it is my hope that more and more people feel comfortable around AVs so that we can unleash the full potential of Connected Mobility.