Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter

Urban intersections put high demands on fully automated vehicles, in particular, if occlusion occurs. In order to resolve such and support vehicles in unclear situations, a popular approach is the utilization of additional information from infrastructure-based sensing systems. However, a widespread use of such systems is circumvented by their complexity and thus, high costs. Within this paper, a generic interface is proposed, which enables a huge variety of sensors to be connected. The sensors are only required to measure very few features of the objects, if multiple distributed sensors with different viewing directions are available. Furthermore, a Labeled Multi-Bernoulli (LMB) filter is presented, which can not only handle such measurements, but also infers missing object information about the objects' extents. The approach is evaluated on simulations and demonstrated on a real-world infrastructure setup

Object-level fusion for surround environment perception in automated driving applications

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. The detection of dynamic objects is one of the most important aspects required by advanced driver assistance systems and automated driving. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system, where processing occurs in three levels: sensor, fusion and application. A complete and consistent object model which includes the object’s dynamic state, existence probability and classification is defined as a sensor-independent and generic interface for sensor data fusion across all three processing levels. Novel algorithms are developed for object data association and fusion at the fusion-level of the architecture. An asynchronous sensor-to-global fusion strategy is applied in order to process sensor data immediately within the high-level fusion architecture, giving driver assistance systems the most up-to-date information about the vehicle’s environment. Track-to-track fusion algorithms are uniquely applied for dynamic state fusion, where the information matrix fusion algorithm produces results comparable to a low-level central Kalman filter approach. The existence probability of an object is fused using a novel approach based on the Dempster-Shafer evidence theory, where the individual sensor’s existence estimation performance is considered during the fusion process. A similar novel approach with the Dempster-Shafer evidence theory is also applied to the fusion of an object’s classification. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. A thorough evaluation of the complete object model is performed on a closed test track using vehicles equipped with hardware for generating an accurate ground truth. Existence and classification performance is evaluated using labeled data sets from real traffic scenarios. The evaluation demonstrates the accuracy and effectiveness of the proposed sensor data fusion approach. The work presented in this thesis has additionally been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic

06311 Abstracts Collection -- Sensor Data and Information Fusion in Computer Vision and Medicine

From 30.07.06 to 04.08.06, the Dagstuhl Seminar 06311 ``Sensor Data and Information Fusion in Computer Vision and Medicine\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. Sensor data fusion is of increasing importance for many research fields and applications. Multi-modal imaging is routine in medicine, and in robitics it is common to use multi-sensor data fusion. During the seminar, researchers and application experts working in the field of sensor data fusion presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. The second part briefly summarizes the contributions

Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area

Track-to-Track Fusion Using Split Covariance Intersection Filter-Information Matrix Filter (SCIF-IMF) for Vehicle Surrounding Environment Perception

International audienceVehicle surrounding environment perception is an important process for many applications. Nowadays, a tendency is to incorporate redundant and complementary sensors into an intelligent vehicle, in order to enhance its perception ability; then an essential issue arises naturally, i.e. what fusion architecture can be used to combine the data from multiple sensors? In this paper, we propose a new track-totrack fusion architecture using the split covariance intersection filter-information matrix filter (SCIF-IMF). The basic idea is to use the IMF (adapted for estimates in split form) to handle the track temporal correlation of each sensor system and to use the SCIF to handle track spatial correlation. The proposed architecture enjoys complete sensor modularity and thus enables flexible self-adjustment. A simulation based comparative study is presented, which shows that the track-totrack fusion architecture using the SCIF-IMF can achieve centralized architecture comparable performance

Track-to-track fusion using split covariance intersection filter-information matrix filter (SCIF-IMF) for vehicle surrounding environment perception

International audienceVehicle surrounding environment perception is an important process for many applications. Nowadays, a tendency is to incorporate redundant and complementary sensors into an intelligent vehicle, in order to enhance its perception ability; then an essential issue arises naturally, i.e. what fusion architecture can be used to combine the data from multiple sensors? In this paper, we propose a new track-totrack fusion architecture using the split covariance intersection filter-information matrix filter (SCIF-IMF). The basic idea is to use the IMF (adapted for estimates in split form) to handle the track temporal correlation of each sensor system and to use the SCIF to handle track spatial correlation. The proposed architecture enjoys complete sensor modularity and thus enables flexible self-adjustment. A simulation based comparative study is presented, which shows that the track-totrack fusion architecture using the SCIF-IMF can achieve centralized architecture comparable performance

Sensor fusion in smart camera networks for ambient intelligence

This short report introduces the topics of PhD research that was conducted on 2008-2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion experiments performed with four distinct applications of Ambient Intelligence

Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems

In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account