Introducing mobile robots into the collaborative assembly process poses unique challenges for ensuring efficient and safe human-robot interaction. Current human-robot work cells require the robot to cease operating completely whenever a human enters a shared region of the given cell, and the robots do not explicitly model or adapt to the behavior of the human. In this work, we present a human-aware robotic system with single-axis mobility that incorporates both predictions of human motion and planning in time to execute efficient and safe motions during automotive final assembly. We evaluate our system in simulation against three alternative methods, including a baseline approach emulating the behavior of standard safety systems in factories today. We also assess the system within a factory test environment. Through both live demonstration and results from simulated experiments, we show that our approach produces statistically significant improvements in quantitative measures of safety and fluency of interaction.

Autonomous vehicles have to be able to predict whether a human driver will wait at an unregulated inner city narrow passage or not to adapt its behaviour accordingly. To this end, a driving simulator study was conducted in which participiants were subjected to different cooperation behaviours during their approach to a narrow passage. They were asked to rate their intention afterwards. From the recorded trajectories, features which are specific to the scenario are derived. Therewith, Random Forest and Conditional Random Field classifiers for both intention and behaviour prediction are trained. The results show that robust prediction of driver intention and behaviour is possible.

As mixed traffic between automated vehicles and human drivers in inner city becomes more prevalent in the near future understanding and predicting drivers’ behavior is important. Additionally, there is a wide variety of inner city intersections. They can differ greatly in traffic density, visibility, number of objects and many more aspects. This difference in complexity has an influence on the behavior of human drivers at intersections. To further understand the effect of complexity we conducted a naturalistic driving field study in inner city traffic with 34 participants. We focused on unsignalized intersections because there is a greater range of possibly ambiguous situations at such intersections than compared to e.g. an intersection regulated by traffic lights. Features describing the behavior (commit distance, drop in velocity and the minimal velocity) are extracted from the driven trajectories. Additionally, we define intersection complexity by several features describing an intersection. These features include both the static (street, visible and driveable width, the visibility of the other streets and the number of trees) and the dynamic environment (entry location and turning direction, numbers of vehicles, vehicles with interaction, vehicles with priority, vehicles having to yield and pedestrians). Based on those we show that the entry location and the turning direction have a significant effect on the behavior features. Additionally, we show that the typical behavior of human drivers can be predicted by the features describing an intersection’s complexity. Finally, the feature set is reduced in dimensionality for a more condensed intersection description. For that we test reduced feature sets as well as feature sets from an autoencoder and show that prediction is feasible with them as well.

Zusammenfassung: Die Verknüpfung von Automatisierung mit Vernetzung von Fahrzeugen weist in langfristiger Betrachtung eine kaum überschätzbare ökonomische, ökologische und gesellschaftliche Relevanz auf. In der langfristigen Vision wird die vernetzte Automatisierung unseren Straßenverkehr grundlegend verändern. Kooperatives Verhalten zwischen Verkehrsteilnehmern wird dadurch automatisiert erfolgen und somit das Regelverhalten bilden. Außerdem eröffnet technische Kooperation im Vergleich zur heutigen manuellen Kooperation zwischen Verkehrsteilnehmern qualitativ verbesserte und neuartige funktionale Möglichkeiten aufgrund der erzielbaren Latenzzeit, Genauigkeit, Eindeutigkeit und Verlässlichkeit automatischen Verhaltens. Entsprechend aufeinander abgestimmte Trajektorien weisen eine höhere erreichbare Dichte, Geschwindigkeit und Sicherheit auf. Bei langfristig zu erwartendem hohem Durchdringungsgrad des Straßenverkehrs mit vernetzten automatischen Verkehrsteilnehmern sind sichere und hocheffiziente Verkehrsflüsse denkbar, die biologischen Schwarmbewegungen nahe kommen. Dieser Überblicksbeitrag beschreibt den Stand der Forschung in den wesentlichen Forschungsfeldern und das von der DFG eingerichtete SchwerpunktprogrammKooperativ interagierende Automobile.

Autonomous driving is a promising technology to, among many aspects, improve road safety. There are however several scenarios that are challenging for autonomous vehicles. One of these are unsignalized junctions. There exist scenarios in which there is no clear regulation as to is allowed to drive first. Instead, communication and cooperation are necessary to solve such scenarios. This is especially challenging when interacting with human drivers. In this work we focus on unsignalized T-intersections. For that scenario we propose a discrete event system (DES) that is able to solve the cooperation with human drivers at a T-intersection with limited visibility and no direct communication. The algorithm is validated in a simulation environment, and the parameters for the algorithm are based on an analysis of typical human behavior at intersections using real-world data.