AI4CCAM: a focus on Advanced AI-driving CCAM sense-plan-act predictive models
Automated Cars must explain their decisions:
- for the safety and comfort of the car driver and passengers
- to inform the other road users such as other vehicles and pedestrians
- to sustain an audit in case of accident
But explanations are often difficult to build, because automated decision-making results sometimes from situation misunderstandings; user acceptance can only come from full transparency. And today’s explanations are only based on quantitative analysis.
AI4CCAM builds explanations from patio-temporal qualitative constraints sequences. This work is especially carried out within the WP2, focusing on Advanced AI-driving CCAM sense-plan-act predictive models.
Simula released a video explaining the work carried out in WP2.