Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. Automated vehicles struggle with the complex and hectic everyday life within those areas. Sensor-equipped smart infrastructures, which can cooperate with vehicles, can benefit automated traffic by extending the perception capabilities of drivers and vehicle perception systems. Additionally, they offer the opportunity to gather reproducible and precise data of a holistic scene understanding, including context information as a basis for training algorithms for various applications in automated traffic. Therefore, we introduce the Infrastructural Multi-Person Trajectory and Context Dataset (IMPTC). We use an intelligent public inner-city intersection in Germany with visual sensor technology. A multi-view camera and LiDAR system perceives traffic situations and road users’ behavior. Additional sensors monitor contextual information like weather, lighting, and traffic light signal status. The data acquisition system focuses on Vulnerable Road Users (VRUs) and multi-agent interaction. The resulting dataset consists of eight hours of measurement data. It contains over 2,500 VRU trajectories, including pedestrians, cyclists, e-scooter riders, strollers, and wheelchair users, and over 20,000 vehicle trajectories at different day times, weather conditions, and seasons. In addition, to enable the entire stack of research capabilities, the dataset includes all data, starting from the sensor-, calibration- and detection data until trajectory and context data. The dataset is continuously expanded and is available online for non-commercial research at https://github.com/kav-institute/imptc-dataset.

An approach for detecting the road boundary on different types of roads without any preliminary knowledge is presented. We fuse information obtained from an algorithm which detects road markings and road edges in images acquired by a video camera as well as data from a radar sensor. Each road marking, each road edge and each road barrier is tracked individually. Hence we can even capture exits or laybys. We use an edge image for road marking detection and texture information for road edge detection. Additional data provided by a radar sensor is used to measure targets referring to static barriers along the road side such as guardrails. The output of each processing unit is fused into a Kalman filter framework, where the confidence of each subsystem influences the innovation of the overall system. The underlying geometric road model comprises parameters for multiple lanes, the flanking road edge as well as the vehicle's relative pose. The work is part of the project Interactive.

The paper presents SwiftSeg, a novel technique for online time series segmentation and piecewise polynomial representation. The segmentation approach is based on a least-squares approximation of time series in sliding and/or growing time windows utilizing a basis of orthogonal polynomials. This allows the definition of fast update steps for the approximating polynomial, where the computational effort depends only on the degree of the approximating polynomial and not on the length of the time window. The coefficients of the orthogonal expansion of the approximating polynomial-obtained by means of the update steps-can be interpreted as optimal (in the least-squares sense) estimators for average, slope, curvature, change of curvature, etc., of the signal in the time window considered. These coefficients, as well as the approximation error, may be used in a very intuitive way to define segmentation criteria. The properties of SwiftSeg are evaluated by means of some artificial and real benchmark time series. It is compared to three different offline and online techniques to assess its accuracy and runtime. It is shown that SwiftSeg-which is suitable for many data streaming applications-offers high accuracy at very low computational costs.

Neural networks are often used to process temporal information, i.e., any kind of information related to time series. In many cases, time series contain short-term and long-term trends or behavior. This paper presents a new approach to capture temporal information with various reference periods simultaneously. A least squares approximation of the time series with orthogonal polynomials will be used to describe short-term trends contained in a signal (average, increase, curvature, etc.). Long-term behavior will be modeled with the tapped delay lines of a time-delay neural network (TDNN). This network takes the coefficients of the orthogonal expansion of the approximating polynomial as inputs such considering short-term and long-term information efficiently. The advantages of the method will be demonstrated by means of artificial data and two real-world application examples, the prediction of the user number in a computer network and online tool wear classification in turning.

In the development phase of perception systems (e.g. for advanced driver assistance systems) general interest is pointing towards the performance of the respective detection and tracking algorithms. One common way to evaluate such systems relies on simulated data which is used as a reference. We present a semi-autonomous method, which allows the extraction of reference data from sensor recordings (including data at least from a camera and a distance measuring sensor device). Furthermore, we show how to combine these reference data with the output from the object detection system and how to derive performance statistics (detection and miss rates) of the system. As the generated reference information can be stored along with the sensor recordings, this method also facilitates the comparison of different software versions or algorithm parameters.

A framework with a tool-supported high-level specification technique is very important for the development of complex signal processing applications containing software-intensive parts (e.g. hybrid systems in automated production processes) in order to provide safe and reliable systems. In this paper we present the concept of a framework, which is an object-oriented CASE-tool offering a graphical specification ability to model and validate a given application and to control its execution. A variety of people having different programming skills is able to use this visual specification technique effectively. Especially users not being interested in implementation details can specify their application on a high abstraction level by connecting reusable and reliable components (modules representing basic algorithms). As a result, complex signal graphs representing the dataflow between the modules are created. The tool supports this software specification technique by automatic type checking for the connections between modules and by changeable module parameters. On the other hand it is easy for software engineers to integrate additional signal processing algorithms into the framework thus building suitable module libraries without considering a specific high-level application.

Only a few time series methods are applicable to signal trend analysis under real-time conditions. The use of orthogonal polynomials for least-squares approximations on discrete data turned out to be very efficient for providing estimators in the time domain. A polynomial extrapolation considering signal trends in a certain time window is obtainable even for high sampling rates. The presented method can be used as a prediction algorithm, e.g. in threshold monitoring systems, or as a trend correction possibility preparing the analysis of the remaining signal. In the theoretical derivation, the recursive computation of orthogonal polynomials allows the development of these fast algorithms for least-squares approximations in moving time windows.