The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

TLDR

Scenario‑based testing of highly automated vehicles relies on real‑world data, but existing measurement methods fail to meet key requirements. The authors propose an aerial measurement method for scenario‑based validation and introduce the highD dataset of German highway vehicle trajectories. The dataset was collected via an aerial platform over six German highway locations, yielding 16.5 h of data on 110 000 vehicles, 45 000 km driven, and 5 600 lane changes, and is publicly available online. The highD dataset contains 110 000 vehicle trajectories, 16.5 h of footage, and 5 600 lane changes, demonstrating substantial quantity, variety, and scenario coverage.

Abstract

Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: http://www.highD-dataset.com.