Highly Automated Learning for Improved Active Safety of Vulnerable Road Users
This work addresses the problem of data efficiency for autonomous vehicle safety, particularly for vulnerable road users, but appears incremental as it builds on existing active learning and adaptation techniques.
The paper tackles the challenge of requiring large amounts of labeled data for machine learning models in highly automated driving by proposing an autonomous learning process that iteratively refines models through detection, novelty detection with active learning, and online adaptation.
Highly automated driving requires precise models of traffic participants. Many state of the art models are currently based on machine learning techniques. Among others, the required amount of labeled data is one major challenge. An autonomous learning process addressing this problem is proposed. The initial models are iteratively refined in three steps: (1) detection and context identification, (2) novelty detection and active learning and (3) online model adaption.