Risk-Based Filtering of Valuable Driving Situations in the Waymo Open Motion Dataset

TL;DR

This paper introduces a risk-based filtering method that identifies valuable and complex driving situations in large datasets, enhancing automated vehicle testing by focusing on high-risk interactions.

Contribution

The paper presents a novel probabilistic risk model that considers both first- and second-order vehicle interactions to select valuable driving scenarios from large datasets.

Findings

Effectively identifies high-risk situations in Waymo dataset

Outperforms baseline interaction metrics in selecting complex scenarios

Enriches data quality for automated vehicle testing

Abstract

Improving automated vehicle software requires driving data rich in valuable road user interactions. In this paper, we propose a risk-based filtering approach that helps identify such valuable driving situations from large datasets. Specifically, we use a probabilistic risk model to detect high-risk situations. Our method stands out by considering a) first-order situations (where one vehicle directly influences another and induces risk) and b) second-order situations (where influence propagates through an intermediary vehicle). In experiments, we show that our approach effectively selects valuable driving situations in the Waymo Open Motion Dataset. Compared to the two baseline interaction metrics of Kalman difficulty and Tracks-To-Predict (TTP), our filtering approach identifies complex and complementary situations, enriching the quality in automated vehicle testing. The risk data is made open-source: https://github.com/HRI-EU/RiskBasedFiltering.