Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations

Michael Kurenkov, Sajad Marvi, Julian Schmidt, Christoph Bernd Rist, Alessandro Canevaro, Hang Yu, Julian Jordan, Georg Schildbach, Abhinav Valada

Keywords: Autonomous Driving, Dataset Analysis

Abstract:

The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is crucial for developing autonomous systems that can replicate safe driving practices and ensure seamless integration into human dominated environments. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets, including Argoverse 2, nuPlan, Lyft, and DeepUrban. By defining and leveraging existing safety and behavior-related metrics such as time to collision, adherence to speed limits, and interactions with other traffic participants, we aim to provide a comprehensive understanding of each dataset’s strengths and limitations. Our analysis focuses on the distribution of data samples, identifying noise, outliers, and undesirable behaviors exhibited by human drivers in both the training
and validation sets. The results underscore the need for applying robust filtering techniques to certain datasets due to high levels of noise and the presence of such undesirable behaviors.

Submission Number: 19