Comap: A synthetic dataset for collective multi-agent perception of autonomous driving

Collective perception of connected vehicles can sufficiently increase the safety and reliability of autonomous driving by sharing perception information. However, collecting real experimental data for such scenarios is extremely expensive. Therefore, we built a computational efficient co-simulation synthetic data generator through CARLA and SUMO simulators. The simulated data contain image and point cloud data as well as ground truth for object detection and semantic segmentation tasks. To verify the superior performance gain of collective perception over single-vehicle perception, we conducted experiments of vehicle detection, which is one of the most important perception tasks for autonomous driving, on this data set. A 3D object detector and a Bird's Eye View (BEV) detector are trained and then test with different configurations of the number of cooperative vehicles and vehicle communication ranges. The experiment results showed that collective perception can not only dramatically increase the overall mean detection accuracy but also the localization accuracy of detected bounding boxes. Besides, a vehicle detection comparison experiment showed that the detection performance drop caused by sensor observation noise can be canceled out by redundant information collected by multiple vehicles

MAVEN Deliverable 6.4: Integration Final Report

This document presents the work that has been performed in WP6 after D6.3, and therefore focussing on the integration sprints 3-6. It describes which parts of the system are implemented and how they are put together. To do so, it builds upon the deliverables created so far, esp. D6.3 and all other deliverables of the underlying work packages 3, 4 and 5. Another important aspect for understanding the content of this deliverable is D2.1 [4] for the scenario definition of the whole MAVEN project, and the deliverables D6.1 [5] and D6.2 [6], which give an overview on the existing infrastructure and vehicles used in MAVEN

Adapting Future Vehicle Technologies for Smart Traffic Control Systems

Traffic control systems are imperative to the everyday function and quality of life for society. The current methods, such as; SCATS, SCOOT and InSync, provide this solution, but with limited flexibility. With the advances in context-aware technologies and wireless vehicular communication as discussed by Maglaras, and the rise of the Internet of Things allowing inexpensive networking of devices current technologies are becoming rapidly outdated. Some examples of such vehicle technologies are discussed in recent studies, namely, social internet of vehicles, and wireless sensing technologies. As the smart city landscape develops, some of these technological advances can be adapted into smart traffic control systems, improving the transport efficiency throughout the road network, while reducing levels of traffic congestion, amount of air pollution, improving quality of life. Although air pollution can be somewhat mitigated with technologies like Stop-Start, Hybrid or Electric, traffic congestion still has negative effect on the quality of life for the drivers, as well as the residence in the affected areas. As it has been outlined before by Glaesar, reducing traffic congestion remains a crucial goal of these future vehicle technologies. Addressing the traffic congestion problem, this chapter reviews existing technologies and future vehicle concepts that can be a good starting point for future studies of implementing a Smart Traffic Control System (STCS), starting by looking at the importance of STCSs, reviewing existing technologies in use with a focus on the most common, and identifying their shortcomings. Afterward, three potential vehicular technologies; V2X (Vehicle-to-X) communication, vehicle cloud computing (VCC) and vehicle social networks (VSNs) , will be reviewed based on previous works, with their applicability in STCSs based on potential efficiency, security and privacy aspects

Kollektive Perzeption in fahrzeugbasierten Ad-hoc Netzwerken

In combination with the current developments in the area of automatically driving vehicles, the introduction of inter-vehicle communication plays a crucial role for realising the long-term objective of what is known as cooperative driving. A cornerstone for the expansion of automated vehicles is their thorough understanding of the current driving environment. For this purpose, each vehicle generates an environment model containing information about other perceived traffic participants and objects. Local perception sensors are important data providers for this model, as they contribute implicit knowledge about the environment. In combination with a direct communication link between traffic participants, explicit knowledge can be added to the environment model as well. The key concept developed within this thesis is called Collective Perception: it focuses on sharing data gathered by local perception sensors of one vehicle with other traffic participants by means of inter-vehicle communication. As a result of this concept, future applications relying on a comprehensive understanding of the current driving environment are made feasible. The analyses presented in this thesis employ a vehicular ad-hoc network (VANET) based on the standardised framework of the European IEEE 802.11p-based ITS G5 protocol stack for inter-vehicle communication. The effectiveness of the technology relies on an existing communication link between a sufficient number of communication partners - the critical mass. The expansion of inter-vehicle communication, however, can be supported by capacitating indirect effects. Collective Perception is one representative of these effects, as the information density within the network between the vehicles is increased, even at low market penetration rates. At the core of Collective Perception stands the introduction of a message format which serves as a vehicle for the exchange of sensor data within a VANET. The development of the message is influenced by two perspectives: First, the vehicle perspective affects the relevant contents of the message required by data-fusion processes and application algorithms. Second, from the network perspective, constraints resulting from the network stack and effects caused by congestion control mechanisms have to be considered. This thesis addresses both perspectives to develop a holistic concept for exchanging sensor data within a VANET.Im Zusammenhang mit den aktuellen Entwicklungen im Themenbereich automatisch fahrender Fahrzeuge spielt die Einführung der Fahrzeug-zu-Fahrzeug-Kommunikation eine zunehmend wichtige Rolle, um langfristig kooperatives Fahren zu realisieren. Eine Voraussetzung für dessen Umsetzung ist dabei die umfassende Wahrnehmung der aktuellen Fahrumgebung. Jedes Fahrzeug erstellt dafür ein sogenanntes Umfeldmodell, welches Informationen über andere Verkehrsteilnehmer und Objekte beinhaltet. Eine wichtige Datenquelle für dieses Modell sind zum einen lokale Umfeldsensoren, welche implizites Wissen über die aktuelle Fahrumgebung beisteuern. Zum anderen kann dem Umfeldmodell bei einer direkten Kommunikationsverbindung mit anderen Verkehrsteilnehmern auch explizites Wissen hinzugefügt werden. Im Rahmen dieser Arbeit wird ein Konzept zur Realisierung der sogenannten kollektiven Wahrnehmung entwickelt: Hierbei wird Fahrzeugen der Austausch lokaler Sensordaten mit anderen Verkehrsteilnehmern unter Verwendung der Fahrzeug-zu-Fahrzeug-Kommunikation ermöglicht. Somit können zukünftige Fahrerassistenzfunktionen auf ein umfassenderes Umfeldmodell zugreifen. Den im Rahmen der Arbeit durchgeführten Analysen liegt ein fahrzeugbasiertes Ad-hoc Netzwerk zugrunde, welches auf dem europäischen IEEE 802.11p basierten ITS G5 Protokollstapel beruht. Die Effektivität der Technologie fußt hierbei auf der Existenz der sogenannten kritischen Masse: Eine ausreichende Anzahl an Kommunikationspartnern muss zugegen sein, damit der Technologie ein Nutzen zugemessen werden kann. Die Verbreitung der Technologie kann jedoch durch indirekte Effekte unterstützt werden. Die kollektive Wahrnehmung ist ein Repräsentant dieser indirekten Effekte, da die Informationsdichte in dem zwischen den Fahrzeugen bestehenden Netzwerk selbst bei niedrigen Marktausstattungsraten erhöht wird. Im Rahmen der Arbeit wird daher ein neues Nachrichtenformat entwickelt, welches von zwei Perspektiven beeinflusst: Die Sicht der fahrzeugseitigen Assistenzsysteme und deren Datenfusionsalgorithmen beeinflusst die notwendigen Inhalte der Nachricht. Weiterhin werden aus der Netzwerksicht durch Mechanismen wie denen der Lastkontrolle und den bestehenden Nachrichtengrößenbeschränkungen spezifische Anforderungen gestellt. Beide Untersuchungen werden dabei in der Arbeit zur Erstellung eines ganzheitlichen Konzeptes für die kollektive Wahrnehmung verbunden