Volume 71, Issue 4 - Special Issue: Cooperative Interacting Automobiles; Guest Editors: Christoph Stiller and Matthias Althoff

Understanding human interactions in today’s transportation system is a prerequisite for developing well-accepted cooperatively interacting autonomous vehicles. This paper is devoted to the two-sided narrow passage scenario and uses trajectory data to investigate drivers’ interaction behavior when encountering each other from opposite directions. Trajectory data of 209 encounters at a road narrowing were analyzed in terms of drivers’ approaching behavior and arrival order. The exploratory analysis has shown that in this specific location an informal traffic rule has developed: It was not the order of arrival but the direction of travel that primarily determined who passed the road narrowing first. This result shows that informal rules can influence drivers’ interaction behavior and should accordingly be considered in the development of autonomous vehicles to ensure safe and efficient encounters with human road users.

Zusammenfassung Eine der Herausforderungen vor der Einführung des automatischen Fahrens sind nicht signalisierte innerstädtische Kreuzungen. Dort kann es zu nicht eindeutig geregelten Verklemmungssituationen kommen. Die Beteiligten müssen in diesem Fall miteinander kooperieren, was für automatische Fahrzeuge eine Herausforderung darstellt. Der vorgestellte Algorithmus ist in der Lage, eine Verhaltensentscheidung für eine automatische Fahrzeugführung zu treffen. Die Entscheidung ist abhängig von der Situation und dem Verhalten der übrigen Verkehrsteilnehmer. Das Modell ist als ereignisdiskretes System modelliert, dies ist insbesondere im Hinblick auf die Nachvollziehbarkeit der Entscheidungen von Vorteil. Der Algorithmus wird mitsamt der verwendeten Eigenschaften und der definierten Ereignisse ausführlich vorgestellt und anhand einer Simulation auf Karten realer Kreuzungen validiert.

Modelling the behaviour of automated vehicles requires an understanding of the acceptance towards certain behaviours by the human cooperation partners. This work addresses the evaluation of two communication means on the motorway slip road from the perspective of drivers in the target lane. In a video study ( N = 68) two implicit communication means (position and duration of lane change) were investigated. The cooperation partner is either a manual vehicle or a car labelled as automated by a status eHMI. The results show no significant differences in the cooperation and criticality ratings between non-automated or automated cooperation partners. A slow lane change is rated as less critical and more cooperative. A non-linear relationship emerges for the position of the change. A change in the middle of the slip road is rated most cooperative and least critical.

As the first automated driving functions are now finding their way into serial production vehicles, the focus of research and development has shifted from purely automated capabilities to cooperative systems, i.e. cooperation between vehicles, and vehicle automation with drivers. Especially in partially and highly automated cooperative driving the driver should be able to take over the driving task or adapt the driving behavior. This paper presents the pattern approach to cooperation as a method to recognize and solve reoccurring problems. As an example, the pattern approach is applied to the use case of a takeover request on a highway. The concept of Confidence Horizons, which balance the capabilities of the driver and the automation based on cooperative interaction patterns, is introduced. To estimate the human capabilities for this Confidence Horizon, a Diagnostic Takeover Request is used, in which the automation analyzes the driver’s orientation reaction to a takeover request. This allows the early detection of potentially unsafe takeovers reducing possible transitions to a Minimum Risk Maneuver (MRM).

For autonomous driving to operate safely it is crucial to perceive surrounding objects correctly. Not only detection but also state estimation (track) of a perceived object is urgent. The state is required to enable a safe motion planning, since it allows to predict the future position of an object. To include only valid information, the state estimations must be maintained to determine which track is active and which is not. Mostly, a simple count-based approach is used. For this, we present an investigation of two common approaches from non-cooperative track management in comparison to two new management strategies to maintain tracks in a cooperative scenario. We evaluate them using three simulated scenarios with a varying rate of cooperative vehicles. A confidence-based approach was able to increase the average precision by up to 9 percentage points.

Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments.

Zusammenfassung Die Bewegungsplanung für automatisierte Fahrzeuge in gemischtem Verkehr, bei dem sich automatisierte und von Menschen gesteuerte Fahrzeuge die Straße teilen, ist eine anspruchsvolle Aufgabe. Um die Komplexität dieser Aufgabe zu reduzieren, gehen moderne Planungsansätze oft davon aus, dass die zukünftige Bewegung umliegender Fahrzeuge unabhängig vom Verhalten des automatisierten Fahrzeugs prädiziert werden kann. Die Trennung der Prädiktion anderer von der eigenen Planung kann, insbesondere in stark interaktiven Verkehrssituationen, zu suboptimalem, übermäßig konservativem Fahrverhalten führen. In dieser Arbeit wird ein Ansatz zur Planung von kooperativem, interaktionsbewusstem Verhalten auf Basis einer Multi-Agenten-Trajektorienplanung vorgestellt. Hierbei wird die Prädiktion anderer sowie die eigene Planung, mittels gemischt-ganzzahliger quadratischer Programmierung gemeinsam gelöst. Unsicherheiten im Verhalten anderer Verkehrsteilnehmer werden mittels unterschiedlicher Intentionsmodelle berücksichtigt. Die Anwendbarkeit des Ansatzes wird anhand numerischer Experimente für ein Fahrstreifenwechselszenario in dichtem Verkehr demonstriert.

Sensor data sharing in V2X communication enables vehicles to exchange locally perceived sensor data with each other to increase their environmental awareness. It relies on the periodic exchange of selected, safety-relevant objects. Object selection is used to reduce channel resource usage. Additionally, vehicles use congestion control mechanisms to avoid overloading the channel. Currently, both object selection and congestion control mechanisms operate independently. We study a congestion-aware object filtering approach combining both and improving the performance of sensor data sharing.

This article presents the testing of distributed trajectory planning algorithms using our rapid prototyping platform, the Cyber-Physical Mobility Lab (CPM Lab). We propose two algorithms for distributed trajectory planning which plan trajectories at intersections, highway on- and off-ramps, and lane changes for networked and autonomous vehicles. The algorithms avoid collisions between vehicles using a synchronization-based and a prioritized Distributed Model Predictive Control (DMPC) strategy. We test two algorithms in the CPM Lab which is able to handle parallel, sequential, and hybrid computations. The CPM Lab achieves reproducible experiments under non-deterministic computation times and stochastic communication times. Our evaluation shows that different algorithms for distributed trajectory planning can be efficiently tested in different in-the-loop tests.