Safety argumentation for a validation dataset

You must be logged in to watch this session

First Name*

Last Name*

Job Title

Company Name

Country of Residence*

Work E-mail Address*

Password*

Confirm Password*

Your personal data will be used to support your experience throughout this website, to manage access to your account, and for other purposes described in our privacy policy.

Sense Media, on behalf of AutoSens, needs the contact information you provide to us to update you with information about AutoSens and our products. You may unsubscribe from these communications at anytime. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, check out our privacy policy.

Only fill in if you are not human

Safety argumentation for a validation dataset

Event: AutoSens Detroit

| Session date: Thursday 11th May

Session date: Thursday 11th May

, 2023

Hear from:

Pooyan Sadeghi

Perception Expert,

Kognic AB

Pooyan Sadeghi

Perception Expert,

Kognic AB

Testing of an ML (Machine Learning) and AI-based perception system shall be realized as a part of the safety assurance of ADAS (Advanced Driver-Assistance Systems) and ADS (Autonomous Driving Systems). A safety-aware perception system to be validated needs the knowledge of an ideal result. This ideal result, a validation dataset, shall contain all the valuable information about the surrounding traffic and environment the perception system should perceive. The validation dataset is fundamental to the reliability and integrity of a perception system. A perception system relies not only on the quality of the validation dataset but also on its integrity. Therefore we need to address it as a part of the perception system safety case.
The presentation will focus on the elements that can expose insufficiencies in the dataset and provide sufficient argumentation on the safety aspects a validation dataset must fulfill to validate a perception system at different stages.