Towards large scale urban traffic reference data: Smart infrastructure in the test area autonomous driving baden-württemberg

Abstract

This paper presents the concept, realization and evaluation of a flexible and scalable setup for smart infrastructure at the example of the Test Area Autonomous Driving Baden-Württemberg. In verification and validation of autonomous driving systems, there exists a gap between virtual validation and real road tests: Simulation provides an easy and efficient way to assess a system’s performance under a variety of environmental constraints, but is restricted to model assumptions and scenarios, which might ignore important aspects. Whereas expensive real road tests promise an unexpected environment for statistical evaluation of traffic scenarios, but lack of observability. Our setup for smart infrastructure is supposed to close the gap by tackling this issue by observing and providing reference data of traffic scenarios for application in different testing and evaluation settings. We present the approach of implementing a distributed intelligent infrastructure capable of handling traffic light states, road topology and especially information about locally observed traffic participants. The data is provided online via Vehicle-to-X (V2X) communication for live testing and sensor range extension as well as offline via a backend for high-precision analysis and application of machine learning techniques. To obtain information about traffic participants, a camera based object tracking was realised. To cope with the high amount of information to be transmitted via V2X and to use the available bandwidth optimally, the standard for broadcasting vehicle information is modified by applying a form of data compression through prioritization. The setup is initially evaluated at a large intersection in Karlsruhe, Germany.