Keller C.G.,University of Heidelberg |
Dang T.,Daimler Research and Development |
Fritz H.,Daimler Research and Development |
Joos A.,Daimler Research and Development |
And 2 more authors.
IEEE Transactions on Intelligent Transportation Systems | Year: 2011
Active safety systems hold great potential for reducing accident frequency and severity by warning the driver and/or exerting automatic vehicle control ahead of crashes. This paper presents a novel active pedestrian safety system that combines sensing, situation analysis, decision making, and vehicle control. The sensing component is based on stereo vision, and it fuses the following two complementary approaches for added robustness: 1) motion-based object detection and 2) pedestrian recognition. The highlight of the system is its ability to decide, within a split second, whether it will perform automatic braking or evasive steering and reliably execute this maneuver at relatively high vehicle speed (up to 50 km/h). We performed extensive precrash experiments with the system on the test track (22 scenarios with real pedestrians and a dummy). We obtained a significant benefit in detection performance and improved lateral velocity estimation by the fusion of motion-based object detection and pedestrian recognition. On a fully reproducible scenario subset, involving the dummy that laterally enters into the vehicle path from behind an occlusion, the system executed, in more than 40 trials, the intended vehicle action, i.e., automatic braking (if a full stop is still possible) or automatic evasive steering. © 2011 IEEE.
Keller C.G.,University of Heidelberg |
Gavrila D.M.,Daimler Research and Development |
Gavrila D.M.,University of Amsterdam
IEEE Transactions on Intelligent Transportation Systems | Year: 2014
Future vehicle systems for active pedestrian safety will not only require a high recognition performance but also an accurate analysis of the developing traffic situation. In this paper, we present a study on pedestrian path prediction and action classification at short subsecond time intervals. We consider four representative approaches: two novel approaches (based on Gaussian process dynamical models and probabilistic hierarchical trajectory matching) that use augmented features derived from dense optical flow and two approaches as baseline that use positional information only (a Kalman filter and its extension to interacting multiple models). In experiments using stereo vision data obtained from a vehicle, we investigate the accuracy of path prediction and action classification at various time horizons, the effect of various errors (image localization, vehicle egomotion estimation), and the benefit of the proposed approaches. The scenario of interest is that of a crossing pedestrian, who might stop or continue walking at the road curbside. Results indicate similar performance of the four approaches on walking motion, with near-linear dynamics. During stopping, however, the two newly proposed approaches, with nonlinear and/or higher order models and augmented motion features, achieve a more accurate position prediction of 10-50 cm at a time horizon of 0-0.77 s around the stopping event. © 2013 IEEE.
Schlechtriemen J.,University of Siegen |
Wabersich K.P.,Daimler Research and Development |
Kuhnert K.-D.,University of Siegen
IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2016
The vision of autonomous driving is piecewise becoming reality. Still the problem of executing the driving task in a safe and comfortable way in all possible environments, for instance highway, city or rural road scenarios is a challenging task. In this paper we present a novel approach to planning trajectories for autonomous vehicles. Hereby we focus on the problem of planning a trajectory, given a specific behavior option, e.g. merging into a specific gap at a highway entrance or a roundabout. Therefore we explicitly take arbitrary road geometry and prediction information of other traffic participants into account. We extend former contributions in this field by providing a flexible problem description and a trajectory planner without specialization to distinct classes of maneuvers beforehand. Using a carefully chosen representation of the dynamic free space, the method is capable of considering multiple lanes including the predicted dynamics of other traffic participants, while being real-time capable at the same time. The combination of those properties in one general planning method represents the novelty of the proposed method. We demonstrate the capability of our algorithm to plan safe trajectories in simulation and in real traffic in real-time. © 2016 IEEE.
Holzer M.,Daimler Research and Development |
Hammer D.,Daimler Research and Development |
Rothenberg S.,Daimler Research and Development |
Sommer-Dittrich T.,Daimler Research and Development |
And 4 more authors.
International SAMPE Technical Conference | Year: 2016
Carbon fiber reinforced plastic (CFRP) composites are increasingly used in the automotive industry due to the high lightweight potential. In the course of vehicle electrification, there is a rising interest in wireless charging technologies at the same time. Automotive wireless charging systems are based on inductive power transfer (IPT). Magnetic fields in the low kilohertz frequency range are used to transfer power from the primary side on the ground to the secondary unit under the car body. The vehicle integration of IPT charging units requires information about the magnetic shielding properties of the surrounding materials, whether if there is high shielding or a significant permeability. This paper studies the shielding properties of continuous carbon fiber reinforced plastic in magnetic near fields with low kilohertz frequency. The shielding of alternating magnetic near fields is based on the induction of eddy currents in the material structure. The eddy current flow and therefore the shielding behavior depend on the electrical conductivity and its anisotropy. A novel experimental setup and a new contacting technique have been developed to characterize the electrical conductivity of CFRP in the fiber plane. An extensive experimental study on the anisotropic electrical conductivity of CFRP structures of different lay-up has been conducted. Thereby unidirectional, orthotropic and quasi-isotropic laminates have been investigated and evaluated concerning the magnetic shielding properties. In addition to the electrical conductivity research, a magnetic near field measurement cell was used to study the local shielding behavior. Similar composite types were tested in the frequency range of 30-1000 kHz. The results have been qualitatively compared and contrasted with the shielding properties of copper and aluminum specimen. Copyright 2016 by Daimler Research and Development. Published by the Society for the Advancement of Material and Process Engineering with permission.
Flohr F.,Daimler Research and Development |
Flohr F.,University of Amsterdam |
Dumitru-Guzu M.,Daimler R and D |
Dumitru-Guzu M.,Technical University of Delft |
And 2 more authors.
IEEE Transactions on Intelligent Transportation Systems | Year: 2015
We present a probabilistic framework for the joint estimation of pedestrian head and body orientation from a mobile stereo vision platform. For both head and body parts, we convert the responses of a set of orientation-specific detectors into a (continuous) probability density function. The parts are localized by means of a pictorial structure approach, which balances part-based detector responses with spatial constraints. Head and body orientations are estimated jointly to account for anatomical constraints. The joint single-frame orientation estimates are integrated over time by particle filtering. The experiments involved data from a vehicle-mounted stereo vision camera in a realistic traffic setting; 65 pedestrian tracks were supplied by a state-of-the-art pedestrian tracker. We show that the proposed joint probabilistic orientation estimation framework reduces the mean absolute head and body orientation error up to 15° compared with simpler methods. This results in a mean absolute head/body orientation error of about 21°/19°, which remains fairly constant up to a distance of 25 m. Our system currently runs in near real time (8-9 Hz). © 2000-2011 IEEE.