IT Designers GmbH

Esslingen, Germany

IT Designers GmbH

Esslingen, Germany

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Palmer J.,IT Designers GmbH | Rehborn H.,Daimler AG | Kerner B.S.,Daimler AG
Traffic Engineering and Control | Year: 2011

The ASDA and FOTO models developed in the framework of Kerner's three-phase traffic theory exhibit high quality recognition, tracking, and prediction of congested traffic patterns in space and time; for this reason, the ASDA/FOTO models have been used since 2000 in a variety of on-line installations in which traffic data are measured with stationary road detectors. In this article, the ASDA and FOTO models are further developed for the reconstruction, tracking, and prediction of spatiotemporal congested traffic patterns based on probe vehicle data only. The suitability and applicability of these new versions of ASDA and FOTO models is evaluated and proven by using results of microscopic simulations.


Gehrig S.,Daimler AG | Schneider N.,IT Designers GmbH | Franke U.,Daimler AG
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops | Year: 2014

Advanced Driver Assistance Systems benefit from a full 3D reconstruction of the environment in real-time, often obtained via stereo vision. Semi-Global Matching (SGM) is a popular stereo algorithm for solving this task which is already in use for production vehicles. Despite this progess, one key challenge remains: stereo vision during adverse weather conditions such as rain, snow and low-lighting. Current methods generate many disparity outliers and false positives on a segmentation level under such conditions. These shortcomings are alleviated by integrating prior scene knowledge. We formulate a scene prior that exploits knowledge of a representative traffic scene, which we apply to SGM and Graph Cut based disparity estimation. The prior is learned from traffic scene statistics extracted during good weather. Using this prior, the object detection rate is maintained on a driver assistance database of 3000 frames including bad weather while reducing the false positive rate significantly. Similar results are obtained for the KITTI dataset, maintaining excellent performance in good weather conditions. We also show that this scene prior is easy and efficient to implement both on CPU platforms and on reconfigurable hardware platforms. The concept can be extended to other application areas such as indoor robotics, when prior information of the disparity distribution is gathered. © 2014 IEEE.


Rehborn H.,Daimler AG | Klenov S.L.,Moscow Institute of Physics and Technology | Palmer J.,IT Designers GmbH
Physica A: Statistical Mechanics and its Applications | Year: 2011

Based on real traffic data measured on American, UK and German freeways, we study common features of traffic congestion. We have found that traffic features [J] and [S] defining traffic phases "wide moving jam" (J) and "synchronized flow" (S) in Kerner's three-phase theory are indeed common spatiotemporal traffic features observed in the UK, the USA and Germany. For the testing of Kerner's "line J", representing the propagation of the wide moving jam's downstream front, four different methods for a study of moving jam propagation in empirical data are studied and compared for each congested traffic situation occurring in the three countries. A statistical study of velocities of wide moving jam fronts is presented, which has been performed through the analysis of database containing more than 280.000 min of observed wide moving jams measured on about 1200 km long freeway network in Hessen (Germany) during more than two years. © 2011 Elsevier B.V. All rights reserved.


Rehborn H.,Daimler AG | Koller M.,IT Designers GmbH
Journal of Advanced Transportation | Year: 2014

On the basis of real traffic and environmental data measured on German freeways, we studied common features of traffic congestion under the influence of severe weather conditions. We have found that traffic features [J] and [S] defining traffic phases "wide moving jam" (J) and "synchronized flow" (S) in Kerner's three-phase theory are indeed common spatiotemporal traffic features. The quantitative parameters for both traffic phases [S] and [J] were investigated in a comparison of "ideal" weather conditions (good visibility and no precipitation) and severe weather situations (icy road, wind, precipitation, etc.). We showed spatiotemporal congested patterns in several space-time diagrams based on the Automatic Tracking of Moving Jams/Forecasting of Traffic Objects (ASDA/FOTO) model reconstruction for roadside detectors. A statistical study of traffic phase [J] parameters was presented, showing the average values and standard deviation of the quantities. Similarities and differences were analyzed, and some consequences for vehicular applications were discussed to cope with severe weather conditions. © 2013 John Wiley & Sons, Ltd.


Muffert M.,Daimler AG | Schneider N.,IT Designers GmbH | Franke U.,Daimler AG
Proceedings - Conference on Computer and Robot Vision, CRV 2014 | Year: 2014

In summer 2013, a Mercedes S-Class drove completely autonomously for about 100 km from Mannheim to Pforzheim, Germany, using only close-to-production sensors. In this project, called Mercedes Benz Intelligent Drive, stereo vision was one of the main sensing components. For the representation of free space and obstacles we relied on the so called Stixel World, a generic 3D intermediate representation which is computed from dense disparity images. In spite of the high performance of the Stixel World in most common traffic scenes, the availability of this technique is limited. For instance under adverse weather, rain or even spray water on the windshield results in erroneous disparity images which generate false Stixel results. This can lead to undesired behavior of autonomous vehicles. Our goal is to use the Stixel World for a robust free space estimation and a reliable obstacle detection even during difficult weather conditions. In this paper, we meet this challenge and fuse the Stixels incrementally into a reference grid map. Our new approach is formulated in a Bayesian manner and is based on existence estimation methods. We evaluate our new technique on a manually labeled database with emphasis on bad weather scenarios. The number of structures which are detected mistakenly within free space areas is reduced by a factor of two whereas the detection rate of obstacles increases at the same time. © 2014 IEEE.


Pfeiffer D.,Daimler AG | Gehrig S.,Daimler AG | Schneider N.,IT Designers GmbH
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2013

Applications based on stereo vision are becoming increasingly common, ranging from gaming over robotics to driver assistance. While stereo algorithms have been investigated heavily both on the pixel and the application level, far less attention has been dedicated to the use of stereo confidence cues. Mostly, a threshold is applied to the confidence values for further processing, which is essentially a sparsified disparity map. This is straightforward but it does not take full advantage of the available information. In this paper, we make full use of the stereo confidence cues by propagating all confidence values along with the measured disparities in a Bayesian manner. Before using this information, a mapping from confidence values to disparity outlier probability rate is performed based on gathered disparity statistics from labeled video data. We present an extension of the so called Stixel World, a generic 3D intermediate representation that can serve as input for many of the applications mentioned above. This scheme is modified to directly exploit stereo confidence cues in the underlying sensor model during a maximum a posteriori estimation process. The effectiveness of this step is verified in an in-depth evaluation on a large real-world traffic data base of which parts are made publicly available. We show that using stereo confidence cues allows both reducing the number of false object detections by a factor of six while keeping the detection rate at a near constant level. © 2013 IEEE.


Theissler A.,IT Designers GmbH | Dear I.,Brunel University
Proceedings of the IADIS International Conference Intelligent Systems and Agents 2012, ISA 2012, IADIS European Conference on Data Mining 2012, ECDM 2012 | Year: 2012

In modern vehicles 40 to 80 electronic control units are interconnected via the in-vehicle network. During test drives the network communication is recorded in order to locate faults, resulting in a multivariate time series with millions of data points for each test drive. Hence, manually analysing each recording in great detail is not feasible. This work addresses the question: How can we cope with the soaring data volume and complexity of recordings from test drives caused by the ever-increasing complexity of vehicles? This paper proposes to use machine learning to support domain-experts by pointing them to the relevant parts in the recordings, i.e. anomalies. One cannot assume to have a training set with representative anomalies, because this would imply that knowledge of all fault states and corresponding recordings exist. So the idea is to (1) learn normal behaviour from recordings, and (2) autonomously report deviations as anomalies, which corresponds to a one-class classification problem. The paper discusses practical solutions for all steps ranging from data acquisition to classification utilising support vector data description as a classifier. Abnormal subsequences are detected by shifting a non-overlapping window over the classifier's output. The idea is validated on data from a controlled environment, a testrig with a DC motor.


Schneider N.,IT Designers GmbH | Gehrig S.,Daimler AG | Pfeiffer D.,Daimler AG | Banitsas K.,Brunel University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

The accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground-truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the Stixel World vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km. © 2012 Springer-Verlag.


Rehborn H.,Daimler AG | Klenov S.L.,Moscow Institute of Physics and Technology | Palmer J.,IT Designers GmbH
IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2011

Based on real traffic data measured on American, UK and German freeways, we study Kerner's common features of traffic congestion phases (synchronized flow and wide moving jam) relevant for many transportation engineering applications. General features of traffic congestion, i.e., features of traffic breakdown and of the further development of congested regions, are shown on freeways in the USA and UK beyond the previously known data examples. For the testing of Kerner's "line J", representing the propagation of the wide moving jam's downstream front, four different methods are studied and compared for each congested traffic situation occurring in the three countries. © 2011 IEEE.


Savasturk D.,Environment Perception | Froehlich B.,Environment Perception | Schneider N.,IT Designers GmbH | Enzweiler M.,Environment Perception | Franke U.,Environment Perception
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | Year: 2015

Robust knowledge about other vehicles around the ego-vehicle is fundamental for most advanced driver assistance systems. Typically, this task is solved by radar, lidar, mono or stereo camera systems. To get a higher accuracy, a combination of multiple sensors is proposed in this work. Infrared cameras are already available in many passenger cars, mainly for night vision purposes, e.g. detecting pedestrians or animals on the road. In this paper, we analyze the benefit of combining stereo-vision in the visible domain with monocular vision in infrared images. We use the task of vehicle detection as an experimental setting. In extensive experiments involving more than eight hours of driving, we demonstrate that the additional detection of vehicles in infrared images significantly improves the overall integrated system performance. © 2015 IEEE.

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